Jobs in United States

Machine Learning Engineer in United States

703 active opportunities · Updated October 2026

Explore current machine learning engineer jobs across United States. Filter by work mode, employment type, experience, department, date posted and distance.

Hiring demand

41/100

watch · 51 related jobs

Hiring trend

-75.6%

Job postings compared with the previous 30 days

Remote options

25.5%

Share of matching jobs listed as remote

Typical salary

$158.4K – $158.4K/yr

Based on 6 salary observations

B
📍 San Francisco, California, United States· Full-time
✓ Quality checkedCompany trend -80.4%

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. THE ROLE Baseten is building its own GPU infrastructure for large-scale inference. As we move into large scale, high-density NVIDIA systems, the hardest failures are intermittent, cross-layer, and difficult to prove: RoCE congestion, InfiniBand stalls, ECN/DCQCN mis-tuning, bad optics, RNIC issues, host kernel stalls, GPU driver problems, and workload symptoms that look like network problems, but are not. We are hiring a Lead Software Engineer to build a first-class observability and root-cause analysis system for GPU fabrics. This is a hard distributed systems problem, not a dashboarding problem. The system will collect high-volume signals from switches, hosts, active probes, and inference services; reduce and correlate them in real time; understand topology and service ownership; and produce actionable diagnosis while an incident is still unfolding. This role sits at the boundary between networking and inference software. RDMA data paths, GPUDirect transfers, prefill/decode disaggregation, KV cache movement, request routing, and workload backpressure can all create fabric symptoms or hide real fabric failures. The goal is to tell an operator, quickly and with evidence, whether an incident is caused by the fabric, host, NIC, GPU, RDMA path, scheduler, or serving layer — and what to do next. EXAMPLE INITIATIVES Real-time telemetry engine — Build the ingestion, reduction, storage, and query path for high-cardinality fab

KubernetesMachine LearningAIGo
B
📍 San Francisco, California, United States· Full-time
✓ Quality checkedCompany trend -80.4%

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. We are looking for an engineer with strong experience in machine learning and solid foundations in maths and computer science to join our growing Post-Training team at Baseten. Custom models are instrumental to the success of Baseten customers. By inference volume, the overwhelming majority of traffic at Baseten is to and from models that have been post-trained in some way, whether that be through reinforcement learning, supervised finetuning, a recent technique from the literature, or an in-house research technique from Baseten. The Post-Training team is responsible for the success of our customers’ post-trained models, and we employ a wide array of techniques to produce models that are more efficient and higher quality than even the biggest closed source models for the customer’s specific needs. Your role as a research engineer is to build the in-house tooling to support all of this. We care about training a wide spectrum of different model architectures with a variety of techniques efficiently and at scale. At times this involves zooming deep into a particular technical topic, but more often if involves working across the stack as a whole - systems-level concepts like Kubernetes, cgroups, storage systems, and networking topologies, as well as PyTorch distributed tensor computation, and GPU kernels. RECENT RESEARCH Dense, on-policy or both? Repeated kv cache for long-running agents Distillation without the dark – rep

KubernetesMachine LearningAI
B
📍 San Francisco, California, United States· Full-time
✓ Quality checkedCompany trend -80.4%

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. THE ROLE: Baseten’s Model Performance (MP) team is responsible for ensuring the models running on our platform are fast, reliable, and cost‑efficient. As part of this team, you’ll focus on Model APIs — the infrastructure powering our hosted API endpoints for the latest open‑source models. This work spans distributed systems, model serving, and developer experience. You’ll join a small, high‑impact team operating at the intersection of product, model performance, and infra, helping to define how developers interact with AI models at scale. RESPONSIBILITIES: Design, build, and operate the Model APIs surface with focus on advanced inference capabilities: structured outputs (JSON mode, grammar-constrained generation), tool/function calling and multi-modal serving Profile and optimize TensorRT-LLM kernels, analyze CUDA kernel performance, implement custom CUDA operators, tune memory allocation patterns for maximum throughput and optimize communication patterns across multi-GPU setups Productionize performance improvements across runtimes with deep understanding of their internals: speculative decoding implementations, guided generation for structured outputs, custom scheduling and routing algorithms for high-performance serving Build comprehensive benchmarking frameworks that measure real-world performance across different model architectures, batch sizes, sequence lengths, and hardware configurations Productionize performa

KubernetesMachine LearningAIGo
T
📍 San Francisco, California, United States· Full-time
✓ High-confidence listingCompany trend -50%

$155K – $207K/yr

Quick readStrong listing-quality and freshness signals

About Taskrabbit: Taskrabbit is a marketplace platform that conveniently connects people with Taskers to handle everyday home to-do’s, such as furniture assembly, handyman work, moving help, and much more. At Taskrabbit, we want to transform lives one task at a time. As a company we celebrate innovation, inclusion and hard work. Our culture is collaborative, pragmatic, and fast-paced. We’re looking for talented, entrepreneurially minded and data-driven people who also have a passion for helping people do what they love. Together with IKEA, we’re creating more opportunities for people to earn a consistent, meaningful income on their own terms by building lasting relationships with clients in communities around the world. Taskrabbit is a hybrid company with employees distributed across the US and EU and a Built In — Best Places to Work (2022, 2023, 2024, 2025) continually ranked across multiple national and regional categories. Join us at Taskrabbit, where your work will be meaningful, your ideas valued, and your potential unleashed! This role operates on a hybrid schedule requiring two days of in-office collaboration per week. About the Role Reporting to the Infrastructure Director of Engineering, you are a pragmatic security leader who acts as a business enabler rather than a gatekeeper. You thrive in a high-velocity, non-regulated environment where you must define "what good looks like" from scratch and drive security progress with a blend of strong planning skills and strong cross-functional partnerships. You are a technical player-coach who manages the "how" behind engineering initiatives, providing direct guidance to a lean team which needs to navigate trade-offs between quality, depth, and delivery volume. You are just as comfortable planning core initiatives as you are writing a proposal for security program improvements. You have a proven track record of leading a team to achieve compliance with a security framework from scratch (e.g., CIS, SOC2, P

AgileMachine LearningAIGo
O
📍 San Francisco, California, United States· Full-time
✓ Quality checkedCompany trend -82%

About the Team Training Runtime designs the core distributed machine-learning training runtime that powers everything from early research experiments to frontier-scale model runs. With a dual mandate to accelerate researchers and enable frontier scale, we’re building a unified, modular runtime that meets researchers where they are and moves with them up the scaling curve. Our work focuses on three pillars: high-performance, asynchronous, zero-copy tensor and optimizer-state-aware data movement; performant, high-uptime, fault-tolerant training frameworks (training loop, state management, resilient checkpointing, deterministic orchestration, and observability); and distributed process management for long-lived, job-specific and user-provided processes. We integrate proven large-scale capabilities into a composable, developer-facing runtime so teams can iterate quickly and run reliably at any scale, partnering closely with model-stack, research, and platform teams. Success for us is measured by raising both training throughput (how fast models train) and researcher throughput (how fast ideas become experiments and products). About the Role As a Training: ML Framework Engineer, you will work on improving the training throughput for our internal training framework, while enabling researchers to experiment with new ideas. This requires good engineering (for example designing, implementing, and optimizing state-of-the-art AI models), writing bug-free machine learning code (surprisingly difficult!), and acquiring deep knowledge of the performance of supercomputers. In all the projects this role pursues, the ultimate goal is to push the field forward. We’re looking for people who love optimizing performance, understanding distributed systems, and who cannot stand having bugs in their code. Since our training framework is used for large runs with massive numbers of GPUs, performance improvements here will have a large impact. This role is based in San Francisco, CA. We use a

PythonAWSRestMachine Learning
N
📍 Santa Clara, United States
✓ High-confidence listingCompany trend -8%
Quick readStrong listing-quality and freshness signals

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. NVIDIA is seeking best-in-class ASIC Verification Engineers to verify the design and implementation of the world’s leading inference accelerator. This position offers the opportunity to have real impact in a dynamic, technology-focused company impacting product lines ranging from consumer graphics to self-driving cars and the growing field of artificial intelligence. We have crafted a team of outstanding people around the globe. Their mission is to push the frontiers of what is possible today and define the platform for the future of computing. In this position, you will help to build the high-performance processor elements that implement programmable compute and graphics functionality. What you'll be doing: As a key member of our ASIC Verification team, you will verify the design and implementation of inference accelerator You will be responsible for verification of the ASIC design, architecture, reference models and micro-architecture using advanced verification methodologies Understand the design and implementation of your unit, define the verification scope, develop the verification infrastructure and verify the correctness of the design Collaborate with architects, designers, and pre and post silicon verifi

PythonMachine LearningArtificial IntelligenceAI
B
📍 San Francisco, California, United States· Full-time
✓ Quality checkedCompany trend -80.4%

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. THE ROLE Container runtimes were designed for general-purpose software workloads. AI inference is not a general-purpose workload. Running large models at production scale exposes cracks in every layer of the container stack: runtimes unaware of GPU memory constraints, images that take minutes to pull when a model needs to scale to thousands of replicas, and isolation mechanisms that weren't designed for the multi-tenant serving environments that production AI requires. The tools the industry has relied on for a decade weren't built for this, and patching around those limitations at higher layers only goes so far. Baseten owns the entire pipeline, from the moment a developer pushes a model to the moment a request gets a response. That vertical ownership means we can fix these problems at the root. The Runtime Fabrics team is doing exactly that: purpose-building the container runtime and storage layers for AI inference workloads, led by some of the world's top containerd maintainers. As Engineering Manager of the Runtime Fabrics team, you will lead this work, setting technical direction, growing a world-class team of systems engineers, and ensuring the team's output shapes not just Baseten's infrastructure but the open-source container ecosystem at large. If you've contributed to containerd, runc, or related OCI projects and are ready to lead a team solving some of the hardest problems in infrastructure today, we'd love

LinuxMachine LearningAIC++
C
📍 United States· Full-time
✓ Quality checkedCompany trend -100%

At ClickUp, we're building the future of work: the first truly converged AI workspace unifying tasks, docs, chat, calendar, and enterprise search, all supercharged by context-driven AI. We are an AI-native company. Every team member is expected to leverage AI daily, and we evaluate AI fluency as part of our hiring process. Join us and help redefine what's possible. 🚀 Come lead the Identity & Access Management Platform team (IAM Platform) — the foundational layer that decides who can see and do what across all of ClickUp. This team owns the authorization engine, the permission and role model, authentication (SSO, MFA, SCIM, OIDC), sharing primitives, audit logs, and the core data model and APIs that define how customers' work is organized and nested across the product — the foundation every other part of ClickUp is built on. It is backend-heavy, high-blast-radius work. As we move upmarket, this is some of the most important and security-sensitive work we do — enterprises decide whether they can trust us based on how precise, predictable, auditable, and manageable our access controls are. We're looking for a Senior Engineering Manager to lead and grow this team. Your mandate is twofold: deliver an enterprise-grade access management platform — extensible, configuration-driven, correct and auditable by design — and build the team that will carry it , hiring and developing engineers as we invest heavily in enterprise. You'll raise the access, identity, and admin capabilities enterprises depend on to an enterprise-grade standard and keep them there, partnering with a Staff engineer on technical direction while you own delivery, people, and priorities. Just as important: you'll run this team the way ClickUp runs — AI-native . We structure work so AI agents can read it, route it, roll it up, and report on it, so a small team amplified by agents operates at a different scale. There are no status-reporting meetings; agents keep status, progress, and health current from r

AWSMachine LearningAIRust
C
📍 United States· Full-time
✓ Quality checkedCompany trend -100%

At ClickUp, we're building the future of work: the first truly converged AI workspace unifying tasks, docs, chat, calendar, and enterprise search, all supercharged by context-driven AI. We are an AI-native company. Every team member is expected to leverage AI daily, and we evaluate AI fluency as part of our hiring process. Join us and help redefine what's possible. 🚀 This role bridges infrastructure and product engineering: you'll build genuine partnerships across product, AI, and enterprise teams so that ownership is shared and velocity is never blocked by platform constraints. As Director, you'll set a forward-looking cloud vision, proactively align with stakeholders across the business, and ensure the platform scales for multi-shard, multi-region growth while meeting security and compliance commitments (SOC 2, business continuity/disaster recovery, and enterprise security frameworks). The Role: Cost Efficiency: Drive significant annual infrastructure savings by migrating data workloads to EKS and self-hosting key services such as OpenSearch. Ingress Convergence: Deprecate legacy frontend ALBs and consolidate to a single EKS-managed ALB per shard, unblocking faster deployments across the org. Coverage & Bench Depth: Eliminate single points of ownership across Networking, OpenSearch, and Terraform through cross-training and targeted hiring into coverage gaps. Stakeholder Alignment: Stand up a recurring alignment cadence with Product, AI, Enterprise, and Security so infra planning is driven by demand, not ad-hoc interrupts. Automation: Ship automated shard buildout via Backstage to remove manual toil from enterprise scaling. Reliability: Cut P0/P1 incidents attributed to Cloud Platform (DNS, ALB misconfiguration) through hardened ingress patterns and Terraform-policy guardrails, including blocking unauthenticated public endpoints. Roadmap Ownership: Deliver a roadmap covering Agent enablement, centralized IaC, and deployment rollout acceleration, tied to AI and

AWSMachine LearningAIGo
O
📍 San Francisco, California, United States· Full-time
✓ Quality checkedCompany trend -82%

About the Team The Codex Core Agent team builds the kernel of Codex. We own making the agent better, accelerating research, and making those improvements real in production for our users. That means working across the systems that make Codex actually function as an agent in the real world: the production performance envelope around tokens, latency, reliability, cost, and capacity; the core execution loop and interfaces that turn models into useful behavior; the shared infrastructure that enables other teams to build on Codex; and the feedback loops that turn real-world usage into better models and better agent behavior over time. About the Role We’re looking for applied AI engineers to help bring Codex agents from impressive demos to dependable tools. This role is about improving agent performance on real software engineering tasks and closing the gap between research capability and real-world usefulness. You’ll work closely with research, infrastructure, and product to ensure agents are not just powerful, but useful, steerable, and reliable in practice. The job is not only to improve model behavior in isolation, but to turn those improvements into measurable gains in solve rate, usefulness, and economic value for users. What You’ll Do Design and iterate on agent behaviors across real-world coding tasks and long-horizon workflows. Work closely with research to develop and run evals to measure agent performance, regressions, failure modes, and edge cases. Improve performance through prompting, tool-use strategies, context construction, and model-facing experimentation. Analyze failures in production and systematically improve robustness and reliability. Build feedback loops and data systems that get better real-task data into evaluation and research. Work with product teams to shape user-facing agent experiences and the interfaces the agent depends on. Help define what “good” looks like for agents completing complex tasks end-to-end. You Might Be a Good Fit If You Ha

PythonAWSRestMachine Learning
P
📍 San Francisco, California, United States· Full-time
✓ High-confidence listingCompany trend -100%
Quick readStrong listing-quality and freshness signals

Who Are We? Postman is the world’s leading API platform, used by more than 45 million+ developers and 500,000 organizations, including 98% of the Fortune 500. Postman is helping developers and professionals across the globe build the API-first world by simplifying each step of the API lifecycle and streamlining collaboration—enabling users to create better APIs, faster. The company is headquartered in San Francisco and has offices in Boston, New York, Austin, Tokyo, London, and Bangalore - where Postman was founded. Postman is privately held, with funding from Battery Ventures, BOND, Coatue, CRV, Insight Partners, and Nexus Venture Partners. Learn more at postman.com or connect with Postman on X via @getpostman. P.S: We highly recommend reading The "API-First World" graphic novel to understand the bigger picture and our vision at Postman. The Opportunity As the Head of AI Platform Engineering at Postman, you will lead the alignment of AI development with our growing API platform. You will drive the AI roadmap with a focus on expanding AI-driven API collaboration and agentic capabilities across the platform. This role requires a leader who can identify market opportunities, coordinate cross-functional AI initiatives, and foster strong partnerships to amplify the Postman AI platform's impact What You’ll Do Lead the development and execution of Postman’s AI platform strategy, focused on API ecosystem growth and platform innovation. Drive the AI roadmap, concentrating on API integration, platform expansion, and AI-driven agent functionality. Identify and capitalize on market opportunities for AI-enhanced API collaboration and intelligent agent features. Collaborate closely with business units, product teams, engineering, and external partners to ensure alignment and successful AI initiatives deployment. Oversee implementation with core AI platforms (OpenAI, Anthropic, AWS, etc)), ensuring technical and strategic alignment with AI features and API lifecycle improvements.

AWSMachine LearningAIGo
M
📍 Atlanta, United States
✓ High-confidence listingCompany trend +212.5%
Quick readStrong listing-quality and freshness signals

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Senior Sales Engineer Specialist Our Purpose: We work to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential. Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. We cultivate a culture of inclusion (https://www.mastercard.us/en-us/vision/who-we-are/diversity-inclusion.html) for all employees that respects their individual strengths, views, and experiences. We believe that our differences enable us to be a better team – one that makes better decisions, drives innovation, and delivers better business results. Overview: Mastercard Identity is the global standard in identity verification and behavioral assessment, providing businesses worldwide the ability to link any digital transaction to a human or entity behind it. Our solutions use complex machine learning to combine features derived from the billions of transactions within our proprietary network and the data from our models to deliver industry-leading fraudulent activity detection and risk assessment solutions. As a Sales

PythonSQLMachine LearningRecruitment
S
📍 Bellevue, Washington, United States· Full-time
✓ High-confidence listingCompany trend -93.3%
Quick readStrong listing-quality and freshness signals

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Where Data Does More. Join the Snowflake team. Join our ML Feature Store team where we're building cutting-edge product capabilities that power complex feature transformations and low latency feature serving. We're revolutionizing machine learning feature management and serving capabilities as part of the Snowflake ML suite of products. In the era of GenAI and agents, our team delivers high-quality, fresh feature solutions that make a real difference for our customers. IN THIS ROLE AT SNOWFLAKE, YOU WILL: Help define and own the roadmap for Snowflake Feature Store, working collaboratively with senior architects and ML team leadership Build and execute a vision for incorporating new advances in machine learning Ensure operational excellence of services and meet reliability, availability, and performance commitments Collaborate across ML partner teams to improve development velocity and capabilities Support team members in delivering high technical quality WE WOULD LOVE TO HEAR FROM YOU IF YOU HAVE: 10+ years of experience in designing and building data serving infrastructure and/or machine learning platforms. Strong track record working with machine learning systems and platforms. Strong understanding of computer science fundamentals. B.Sc . in Computer Science Fluency in Ja

PythonJavaMachine LearningAI
R
📍 San Mateo, CA, United States· Full-time
✓ High-confidence listingCompany trend -100%

From $260.3K/yr

Quick readStrong listing-quality and freshness signals

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. Why Safety? At Roblox, we strive to connect a billion people with optimism and civility, and the Safety organization’s mission is to become the leader in civil immersive online communities. We systematically and proactively work to detect, remove, and prevent problematic content and behavior. We seek to influence and shape the product roadmap and prioritization, build safety products, and measure our impact on the community of

AWSGitMachine LearningAI
P
📍 San Francisco, California, United States· Full-time
✓ Quality checkedCompany trend -72.3%

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. Plaid Protect is a real-time fraud intelligence product built on a unique advantage: Plaid’s network-level visibility across bank accounts, devices, identities, sessions, institutions, applications, and financial behavior. Protect helps customers detect first-party fraud, synthetic identities, account takeovers, and coordinated attacks that are difficult to see from a single application, account, or transaction. Trust Index turns that fraud intelligence into real-time fraud scores and actionable attributes. This team builds the systems that make this intelligence possible: low-latency inference, new data and model integrations, customer-facing APIs and attributes, safe rollouts, and feedback loops. Ti3 expanded Plaid’s fraud graph nearly 10x and, in early testing, detected up to 41% more fraud at the same false-positive rate. Learn more about Ti2 and Ti3 . We are a small, high-agency team working closely with Product, Data Science, and Machine Learning. We value demos over docs, conviction over consensus/alignment, builder schedule over meeting-heavy calendars. We’re scrappy and a talent-dense team that has high agency and high ownership. As a Staff Software Engineer on the Protect Core team, you wi

AWSRestMachine LearningAI

Related career options

Similar roles with stronger pay

Client Service Associate

Demand 46/100 · 8 jobs

$840K – $840K/yr

Salary →

$840K – $840K/yr

Salary →
Director of Product

Demand 43/100 · 6 jobs

$382.5K – $382.5K/yr

Salary →
Physical Design Engineer

Demand 43/100 · 8 jobs

$300K – $300K/yr

Salary →
Sr. Engineer

Demand 42/100 · 7 jobs

$300K – $300K/yr

Salary →
Senior Director

Demand 40/100 · 22 jobs

$278.9K – $278.9K/yr

Salary →
🔔

Get new machine learning engineer jobs in United States by email

Daily job updates · Unsubscribe anytime