Machine Learning Engineer Jobs

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

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

Job Details: Job Description: Intel Foundry Automation - Back End Automation Group is seeking a talented student to support our Automation Integrators in advancing manufacturing automation systems. Key Responsibilities: • Assist Automation Integrators with troubleshooting, system upgrades, user training, and root cause analysis to improve efficiency and reduce waste. • Enable Automation Integrators in designing, developing, testing, and debugging software for factory operations, wafer processing, and packaging across multiple software layers. • Support client-based Station Controller systems through validation, troubleshooting, and quality control. Collaborate with global cross-functional teams to drive automation projects and integrate machine learning and AI solutions as needed. Qualifications: Candidates must be currently pursuing a bachelor's degree in computer science, Data Science, Computer Engineering, or a related discipline. They should possess strong analytical, problem-solving, and communication skills, along with hands-on programming experience in languages such as Python, C, and C#. Knowledge of Agile software development methodologies and experience with manufacturing systems are highly desirable. Job Type: Student / Intern Shift: Shift 1 (Malaysia) Primary Location: Malaysia, Penang Additional Locations: Malaysia, Kulim Posti

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

Reolink , a leader in intelligent visual technology for homes and businesses, was founded in 2009 by a group of engineers with a strong commitment to and passion for smarter security solutions. Our products are now trusted by millions of users across more than 110 countries and regions worldwide. Building on this trust, we continue expanding our presence and bringing our innovations to more markets around the globe. Reolink remains committed to delivering advanced, reliable, and user‑centric solutions that empower people to protect what matters most. AI Algorithms Engineer (PHD Holder Only) 5 Work Days Per Week Office Near Tai Seng MRT, Singapore Medical Benefits Provided Entitled to Yearly Bonus & Performance Bonus Job Requirements: PHD Holder in Computer Science, Applied Mathematics, Electrical Engineering, Pattern Recognition, Artificial Intelligence, Automatic Control, Operations Research, Biology, Physics / Quantum Computing, Neuroscience, Statistics or a related field. Familiar with common machine learning and deep learning algorithms and keeping track with the latest SOTA implementations. Strong programming skill in Python, C / C++, proficient in mathematical / statistical concepts and exceptional coding skills Hands-on experience with AI / ML frameworks be familiar such as Caffe, PyTorch, TensorFlow, MxNet etc. Have rich project experience in machine learning and deep learning, be familiar with common algorithm models, such as CNN, RNN, LSTM, Transformer, ViT, etc., and be able to improve and innovate models according to actual problems. Experience in familiar the design, parameter tuning and optimization methods of neural network models is a plus Experience in model compression and in the transplantation and optimization of deep learning forward inference on various platforms, including NPU / GPU / DSP / ARM on mobile platforms and CPU / GPU on server platforms is also a plus. Strong logical thinking and problem-solving ability, able to independen

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

About the Role & Team We’re looking for an Engineering Manager to lead the Data Infrastructure team within Statsig Experiment at Amplitude. You will lead a multidisciplinary team of software engineers, data engineers, and data scientists responsible for the systems that power experimentation at scale. The team owns three critical areas: Data ingestion: Collecting and importing experiment exposures, custom events, OpenTelemetry data, and real user monitoring data across SDKs, streaming systems, cloud storage, and customer data warehouses. Data computation: Building distributed computation systems that transform raw data into accurate, timely experiment results. Stats engine: Developing and productionizing the statistical methods that help customers make trustworthy decisions from their experiments. This is not a traditional data engineering management role. We are looking for a leader with a solid data science and statistical foundation who can connect advances in experimentation methodology with scalable production systems. You will help set our technical and scientific direction, translating new statistical methods and machine learning research into capabilities that customers can use reliably at scale. You’ll partner closely with data scientists, engineers, product managers, and customers to advance the state of experimentation. The ideal candidate is equally comfortable discussing causal inference and statistical power with data scientists, distributed computation architectures with engineers, and experimentation strategy with customers. What You’ll Do Lead and grow the team responsible for Statsig’s data ingestion, experiment computation, and stats engine. Define the technical and scientific strategy for advancing experimentation across both Statsig Cloud and warehouse-native deployments. Partner with data scientists and engineers to turn new statistical and causal inference methods into scalable, reliable product capabilities. Evolve our data and computatio

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

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

Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma’s platform helps teams bring ideas to life—whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us! Figma’s Observability engineering team builds and operates the systems that give us deep visibility into the health, performance, and efficiency of our platform. From metrics, logs, and traces to cost attribution and budgeting, this team ensures that engineers across Figma can detect issues quickly, understand system behavior at scale, and make informed decisions about reliability and spend. The team owns and evolves our core observability stack—including platforms like Datadog, shared instrumentation libraries, and the agents and operators that power telemetry collection—while continuously raising the bar on signal quality and operational clarity. This team ensures that engineers across Figma can detect issues quickly, understand system behavior at scale, and make informed decisions about reliability. The team owns and evolves our core observability stack—including platforms like Datadog, shared instrumentation libraries, the agents and operators that power telemetry collection, and a host of internally developed components to power AI Trace Observability —while continuously raising the bar on signal quality and operational clarity. As the Engineering Manager for Observability, you will lead a team of five engineers responsible for shaping the future of visibility and efficiency at Figma. You’ll define the strategy for instrumentation standards and cost transparency, drive initiatives to optimize observability footprint and spend, and explore innovative AI-driven approaches to anomaly detection

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. As the Engineering Manager for the Lakehouse Foundation team, you will lead a group of engineers responsible for the foundational data layer that all of Lyft's data systems and emerging AI workloads are built on. The team owns catalog and metadata management, table formats and storage, and the access patterns and gateways through which other engineering teams interact with Lyft's data. As Lyft converges on a unified lakehouse architecture, this team builds and operates the single source of truth that powers analytics, machine learning, experimentation, and every business decision made from data. You will play a key role in shaping the team's technical direction, partnering with peer Data Platform teams on a multi-year platform evolution, and developing engineers who operate with autonomy on systems of significant scale and complexity. Lyft's Infrastructure teams build the foundational systems that the rest of engineering depends on to move fast, ship reliably, and scale efficiently. These are high-leverage roles where the work you and your team do has a multiplicative effect across the company. We're looking for experienced leaders who can balance the discipline of operating critical infrastructure with the curiosity to keep evolving how Lyft builds. Engineering at Lyft is a place where managers and engineers operate with high ownership and strong technical judgment. Our engineers expect their managers to be honest, available, and focused on the work that matters: developing their teams, removing obstacles, and giving people the support they need to do their best work. We build teams that are inclusive, technically rigorous, and have a strong sense of ownership for what they build. Responsibilities: Lead a team responsible for Lyft's foundational data layer, including catalog and metadata management,

Discord has a highly engaged community of millions of daily active users who use the platform for many different reasons, but there’s one thing that nearly everyone does: play video games. Discord plays a uniquely important role in the future of gaming, and we are focused on making it easier and more fun for people to hang out before, during, and after playing games. This team is where reports and signals become decisions. We build the LLM pipelines that review reported content, the systems that carry enforcement out, the appeals path users take when we get it wrong, and the tooling our investigators and content reviewers work in every day. Most safety reviews are now decided by automation instead of a person, at a cost per review that has fallen by more than 90% on mature workflows and at precision and recall above trained human reviewers. What's left to automate is the ambiguous, high-stakes end, and with appeals arriving by the hundreds of thousands, getting users a fast and fair answer matters as much as catching harm. We're hiring an Engineering Manager to lead the team: nine engineers across three squads, each with an experienced tech lead. One builds the automated review systems; another owns enforcement, warnings, and the infrastructure behind appeals; and the final owns tooling that enables analysts to be effective. You'll report to the Director of Engineering for Safety and work closely with our content review operations, machine learning, product, policy, and legal teams. The charter is larger than the current headcount, and we expect the role to grow with it. What you'll do Lead three squads, Review, Enforce, and Tooling through their tech leads, setting direction across automated review, enforcement, and appeals Push automation toward nearly all review decisions without giving up precision or recall against a human baseline, and get engineering out of the loop so the operations team can launch and tune workflows themselves Rebuild appeals: one path for

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

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.

About Eudia: Eudia is redefining the future of legal work with AI-powered Augmented Intelligence, enabling Fortune 500 legal teams to move faster, manage risk more effectively, and unlock new business value. Backed by $105M in Series A funding led by General Catalyst, we’re building a category-defining platform that blends AI-driven automation with human expertise, transforming legal from a cost center into a strategic growth driver. At Eudia, we move fast. Unlike traditional enterprise software, our teams ship solutions in days, not months—delivering real impact for some of the world’s largest companies, including Cargill, Coherent, DHL, and Duracell. We’re solving one of the most complex, unsolved challenges in AI: bringing trust, accuracy, and security to legal automation. We’re a team of builders, operators, and problem-solvers who are passionate about reshaping an industry that has long been resistant to change. If you’re looking for a place where you’ll be challenged, take ownership from day one, and work alongside some of the brightest minds in AI and legal —we’d love to meet you. About the Role: Are you interested in building a high-performance Agentic AI driven legal workflow system that supports our current and future scale of platforms? If so, we are looking for you to join our growing team in India. This person will work from our Bangalore office and actively collaborate with the Palo Alto team. We are looking for a Lead Software Engineer that will help develop the most secure, enterprise-grade software using and innovating the latest in Generative AI. The opportunity to tackle challenges in creating cloud-agnostic solutions, maintaining stringent security and compliance standards, and building scalable, resilient platforms for enterprise applications, data, AI, and search also exist while you will be able to routinely innovate on behalf of our customers, collaborating with the world's top