Ai Ml Infra Systems Engineer Jobs

About Sapience AI Sapience AI is the collective intelligence platform for professional communities. We sit above the CRMs, AMS platforms, and knowledge bases that organizations already run, and we turn the expertise scattered across them into something every member can search, act on, and share. The intelligence a community needs is already inside it. Most organizations just cannot reach it. Knowledge lives in silos, in legacy systems, in the heads of a few experts, and in fragmented records no one can connect. We change that. Our work is grounded in four commitments: technology elevates people and never replaces them, the best expertise is already inside the community, everything is built on trust, and every deployment is purpose-driven for the organization it serves. Let’s achieve more, together. Where this role sits This role owns how Sapience AI serves intelligence at scale. You build and run the inference infrastructure that turns the models and reasoning behind MINERVA and COGENT into fast, reliable, affordable answers for members. You work where models meet production: serving, scaling, latency, and cost. Every time a member asks the platform something, your systems are what make the answer arrive quickly and hold up under load. You sit close to applied AI, research, and platform engineering, and you make the difference between a model that works in a notebook and one that serves a community reliably. Why this role exists Collective intelligence is only useful if it is fast and dependable. Members will not wait, and communities cannot rely on a platform that buckles under load or costs too much to run. Serving modern AI at scale is a hard systems problem: large models, tight latency budgets, expensive hardware, and demand that spikes. It takes real infrastructure engineering to get right. The AI/ML Infrastructure and Systems Engineer owns that problem. You make inference fast, reliable, and affordable, so the intelligence the platform promises actually reache

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team Stripe processes over $1T in payments volume per year, which is roughly 1% of the world’s GDP. The tremendous amount of data makes Stripe one of the best places to do machine learning. The ML Infra team builds services and tools that power every step in the ML lifecycle, including data exploration, feature generation, experimentation, training, deploying, serving ML models, and building LLM applications. With the phenomenal developments happening in the field of AI, we are positioned to accelerate the adoption of AI/ML across all parts of the company by building highly scalable and reliable foundational infrastructure. What you’ll do You will work closely with machine learning engineers, data scientists, and product engineering teams to enable seamless end-to-end experience in building solutions across data, analytics, and AI/ML platforms. You will build the next generation of ML Infra services and major new capabilities that substantially improve ML development velocity and MLOps maturity across the company. Responsibilities Designing and building scalable, reliable, and secure services for notebooks, ML model training, experimentation, serving, and LLM applications across multiple regions. Creating services and libraries that enable ML engineers at Stripe to seamlessly transition from experimentation to production across Stripe’s systems. Working directly with product teams and ML engineers to improve their day-to-day pr

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. Safety and Civility is Roblox's #1 priority, and our mission is to protect our global community. As a Senior Software Engineer on the Asset Safety team, you will serve as a hands-on technical leader building resilient, highly scalable systems to ensure millions of daily user-generated assets remain safe and civil. You will tackle complex engineering challenges across diverse modalities—including 3D content, code, images, and video—operating at platform-wide scale across billions of engagement hours while maintaining efficient, low-frequency high-throughput processing. You will: Architect, build, and optimize end-to-end ML-based safety moderation systems to detect and mitigate problematic content using scalable distributed architectures and event-driven, asynchronous workflows. Deliver production-grade, AI-driven safety products end-to-end in a dynamic environment, driving execution from initial ideation to deployment while aligning key cross-functional stakeholders. Drive technical roadmap decisions, implement robust observability and reliability practices, integrate strict security and compliance standards, and enforce rigorous testing and CI/CD pipelines while mentoring engineering team m

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role: The Safeguards ML Infra team designs, builds, and operates the production infrastructure that powers Claude's safety systems. We own the critical backend services that ensure safety on the token generation path, and we own the operational work of getting those systems safely into production: standing up safeguards for every new model launch, and deploying new safety classifiers as they ship. Every frontier model release runs through this team – we configure, verify, and roll out safeguards across every platform Claude runs on (1P, AWS Bedrock, GCP Vertex, etc.), and we lead incident response when issues arise. This role sits at the center of that operational work. You'll ensure safeguards are properly configured and deployed for model launches and own the off-cycle deployment of new safety classifiers — canarying changes, verifying that the right safeguards are provably live on the right models, and holding rollback authority when something looks wrong. Every launch should also shrink the checklist, and the manual verifications should evolve into a system that runs itself. You'll turn launch runbooks into tooling, hand-built checks into continuous validation, and one-off deploys into a repeatable pipeline. We're looking for engineers with deep experience in production change management at scale — people who have owned deploy pipelines, config management systems, rollout safety, or launch readiness for systems under real production pressure. Familiarity with ML research or transformer architectures is not required — you will learn that on the job. What we prioritize is production judgment: a track record of shipping cha

About Decagon Decagon is the leading conversational AI platform empowering every brand to deliver concierge customer experiences. Our technology enables industry-defining enterprises like Avis Budget Group, Block’s Cash App and Square, Chime, Oura Health, and Hunter Douglas to deploy AI agents that power personalized, deeply satisfying interactions across voice, chat, email, SMS, and every other channel. We’re building a future where customer experiences are being redefined from support tickets and hold music to faster resolutions, richer conversations, and deeper relationships. We’re proud to be backed by world-class investors who share that vision, including a16z, Accel, Bain Capital Ventures, Coatue, and Index Ventures, along with many others. We’re an in-office company, driven by a shared commitment to excellence and velocity. Our values — Just Get It Done, Invent What Customers Want, Winner’s Mindset, and The Polymath Principle — shape how we work and grow as a team. About the Team The Infrastructure team builds and operates the foundations that power Decagon: networking, data, ML serving, developer platform, and real‑time voice. We partner closely with product, data, and ML to deliver high‑scale, low‑latency systems with clear SLOs and great developer ergonomics. We organize around four focus areas: Core Infra: The foundational cloud stack—networking, compute, storage, security, and infrastructure‑as‑code—to ensure reliability, scale, and cost efficiency. Data Infra: Streaming/batch data platforms powering analytics/BI and customer‑facing telemetry, including for customer‑managed and on‑prem environments. ML Infra: GPU and model‑serving platforms for LLM inference with multi‑provider routing and support for on‑prem/air‑gapped deployments. Platform (DevEx): CI/CD, paved paths, and core services that make shipping fast, safe, and consistent across teams. Our mission is to deliver magical support experiences — AI agents working alongside humans to resolve issues

About Ema Ema builds AI Employees for HR, IT and Finance. Our AI Employees take on the busy work across the employee experience, from recruiting, onboarding and benefits to IT support, invoice processing and payroll, so people can spend their time on work that needs them. Founded by former executives from Google, Coinbase, Flipkart, and Okta, our team includes engineers from premier tech companies and graduates of Stanford, MIT, UC Berkeley, CMU, and IITs. We are backed by industry leading investors including Accel, Naspers/Prosus, Creaegis, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz. Headquartered in Silicon Valley and with offices in London, Bangalore and Vancouver, Ema is at the frontier of what Agentic AI can do in production. We ship real systems that run real business processes at scale. Role Overview & Key Responsibilities This is a high-leverage leadership role that spans architecture, execution, and org-building, and will shape the direction of our AI / ML initiatives at Ema. We are seeking an AI / ML technical leader who can take a vision and build it. As a Principal ML Engineer at Ema, you will be a senior technical leader responsible for shaping the machine learning roadmap, architecting large-scale ML systems, driving innovation, and ensuring our mixture of expert models (LLM + SLM + Custom Model) is accurate and performant at scale. You will collaborate across teams (research, product, infra, data, etc.), mentor senior engineers, and influence strategy and execution at company-wide levels. Responsibilities Lead the technical direction of GenAI and agentic ML systems that power enterprise-grade AI agents — spanning reasoning, retrieval, tool use, and integrations across various SaaS products. Architect, design, and implement scalable production pipelines for model training, fine-tuning, retrieval (RAG), agent orchestration, and evaluation — ensuring robustness, latency efficiency, and continuous learning. Define and own the mult

P-1592 At Databricks, we are passionate about enabling data and AI teams to solve the world's toughest problems, from making the next mode of transportation a reality to accelerating medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers, and customer obsessed, we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started. As part of the Unity AI Gateway team, you'll build the control plane that every AI request at Databricks passes through: partner and self-hosted models, agents, coding assistants, and MCP servers. It enforces budgets, routes each request to the right model, and applies guardrails inline. Unity AI Gateway is one of our fastest-growing businesses, moving trillions of tokens per week for thousands of customers. The impact you will have: Build the routing layer that picks the right model for each request across quality, cost, latency, availability, and remaining budget Improve reliability, latency, and efficiency of distributed AI workloads Collaborate with research, platform, infra, and AI/ML teams to deliver seamless end-to-end experiences Shape how developers and data scientists build and interact with AI on Databricks What we look for: 8+ years of experience in backend or infrastructure engineering Strong programming skills in Scala, or Go Experience with distributed systems, high-throughput APIs, or cloud-native infrastructure Familiarity with service-oriented architecture, deployment pipelines, and system observability Strong product and ownership mindset. You care about building the right solution, not just any solution Bonus points for: Experience with real-time serving, AI infrastructure, or GPU orchestration Exposure to platforms like

At Vanta, our mission is to help businesses earn and prove trust. We believe that security should be monitored and verified continuously, and we empower companies to practice better security and prove it with ease. Vanta has a kind and talented team, and while some have prior security experience, many have been successful at Vanta without it. As a Data Engineer, you’ll be responsible for laying the foundation for a best-in-class analytics function. You’ll partner closely with our engineering team and business stakeholders to ensure that our analytics stack and processes meet the business needs today with an eye towards the future. Visit our Vanta Engineering Blog to learn more about what our team is working on! What you’ll do as a Senior Data Engineer at Vanta: Design and deploy data infrastructure needed to drive data-driven decision-making solutions Design and implement complex data orchestration models, modeling metadata, scaling reporting tools for data science and ML products users Be the company’s expert on data administration, data management and scalable data systems Write highly tuned, scalable SQL queries running over large-scale, heterogeneous data warehouses Work with the Product and Enterprise Engineering system teams to structure source systems for reporting consumption across the enterprise Help maintain CDC pipelines to power customer reporting Help develop front end applications to expose analytical data sets enterprise wide How to be successful in this role: Have at least four years of experience working with data and two years of experience in Software Engineering or a related field. Have experience with common analytics tooling (e.g. Stitch/Fivetran, Snowflake/BigQuery/Redshift, dbt, Airflow, Dagster). Have good working knowledge of AWS data infra systems and Terraform. Bring a system-oriented and software engineering mindset to the Data Engineering practice. We’re looking to build frameworks that manage data, and minimize bespoke queries Deep kn

Why join us Brex is the intelligent finance platform that enables companies to spend smarter and move faster in more than 200 markets. By combining global corporate cards and banking with intuitive spend management, bill pay, and travel software, Brex enables founders and finance teams to accelerate operations, gain real-time visibility, and control spend effortlessly. Brex’s AI-native automation and world-class service eliminate manual expense and accounting tasks for customers so they can focus on what matters most. Tens of thousands of the world's best companies run on Brex, including DoorDash, Coinbase, Robinhood, Zoom, Plaid, Reddit, and SeatGeek. Working at Brex allows you to push your limits, challenge the status quo, and collaborate with some of the brightest minds in the industry. We’re committed to building a diverse team and inclusive culture and believe your potential should only be limited by how big you can dream. We make this a reality by empowering you with the tools, resources, and support you need to grow your career. Engineering at Brex Engineering at Brex is about building systems that scale with speed and intention. Our teams span Software, Data, Security, and IT, and operate with high autonomy and deep collaboration. We tackle hard technical problems, own our outcomes, and push for excellence at every level — from architecture to deployment. It’s an environment where engineering is a craft, and builders become leaders. Responsibilities: Lead the technical vision and strategic roadmap for the Application Security team, aligning security objectives with Brex's enterprise growth and high-velocity engineering metrics. Establish technical standards and secure defaults across the entire engineering organization, fostering a culture of collaborative security excellence and bridging product platforms, infra, and trust. Architect and secure novel AI/ML and agentic workflows, applying cutting-edge practices to mitigate risks such as prompt injection, mod

About Anyscale: At Anyscale , we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We’re commercializing Ray , a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI , Uber , Spotify , Instacart , Cruise , and many more, have Ray in their tech stacks to accelerate the progress of AI applications out into the real world. With Anyscale, we’re building the best place to run Ray, so that any developer or data scientist can scale an ML application from their laptop to the cluster without needing to be a distributed systems expert. Proud to be backed by Andreessen Horowitz, NEA, and Addition with $250+ million raised to date. About the role Ray aims to provide a universal API for building distributed applications. To achieve this goal requires a distributed system with high levels of performance and reliability. We're looking for engineers with systems software experience that are interested in contributing to the Ray backend. About the Ray Core Team The Ray Core team develops and maintains the Ray C++ backend (e.g., distributed scheduler, language runtime integration, I/O and memory subsystems). We are responsible for the reliability, scalability, and performance of Ray as well as ensuring that Ray provides the right feature set to support higher level libraries and use cases. The team works on a balance of new features / distributed libraries, test infra improvements, debugging, and longer-term architectural improvements to Ray. A snapshot of projects you can work on: Optimizing performance of large-scale workloads on Ray Stability and stress testing infrastructure Improving fault tolerance (HA) As part of this role, you will: Leading cross-team projects while mentoring junior team members Develop high quality open source software to simplify distributed programming (Ray) Identify, implement, and evaluate architectural improvements

About Anyscale: At Anyscale , we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We’re commercializing Ray , a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI , Uber , Spotify , Instacart , Cruise , and many more, have Ray in their tech stacks to accelerate the progress of AI applications out into the real world. With Anyscale, we’re building the best place to run Ray, so that any developer or data scientist can scale an ML application from their laptop to the cluster without needing to be a distributed systems expert. Proud to be backed by Andreessen Horowitz, NEA, and Addition with $250+ million raised to date. About the role Ray aims to provide a universal API for building distributed applications. To achieve this goal requires a distributed system with high levels of performance and reliability. We're looking for engineers with systems software experience that are interested in contributing to the Ray backend. About the Ray Core Team The Ray Core team develops and maintains the Ray C++ backend (e.g., distributed scheduler, language runtime integration, I/O and memory subsystems). We are responsible for the reliability, scalability, and performance of Ray as well as ensuring that Ray provides the right feature set to support higher level libraries and use cases. The team works on a balance of new features / distributed libraries, test infra improvements, debugging, and longer-term architectural improvements to Ray. A snapshot of projects you can work on: Optimizing performance of large-scale workloads on Ray Stability and stress testing infrastructure Improving fault tolerance (HA) As part of this role, you will: Leading cross-team projects while mentoring junior team members Develop high quality open source software to simplify distributed programming (Ray) Identify, implement, and evaluate architectural improvements

About Anyscale: At Anyscale , we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We’re commercializing Ray , a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI , Uber , Spotify , Instacart , Cruise , and many more, have Ray in their tech stacks to accelerate the progress of AI applications out into the real world. With Anyscale, we’re building the best place to run Ray, so that any developer or data scientist can scale an ML application from their laptop to the cluster without needing to be a distributed systems expert. Proud to be backed by Andreessen Horowitz, NEA, and Addition with $250+ million raised to date. About the role Ray aims to provide a universal API for building distributed applications. To achieve this goal requires a distributed system with high levels of performance and reliability. We're looking for engineers with systems software experience that are interested in contributing to the Ray backend. About the Ray Core Team The Ray Core team develops and maintains the Ray C++ backend (e.g., distributed scheduler, language runtime integration, I/O and memory subsystems). We are responsible for the reliability, scalability, and performance of Ray as well as ensuring that Ray provides the right feature set to support higher level libraries and use cases. The team works on a balance of new features / distributed libraries, test infra improvements, debugging, and longer-term architectural improvements to Ray. A snapshot of projects you can work on: - Optimizing performance of large-scale workloads on Ray - Stability and stress testing infrastructure - Improving fault tolerance (HA) As part of this role, you will: Develop high quality open source software to simplify distributed programming (Ray) Identify, implement, and evaluate architectural improvements to Ray core Improve the testing process for Ray to make re

We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS. As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters. Responsibilities Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads Manage and optimize Slurm-based HPC environments for distributed training of large language models Develop robust APIs and orchestration systems for both training pipelines and inference services Implement resource scheduling and job management systems across heterogeneous compute environments Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands Qualifications Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization Experience with deploying and managing distributed training systems at scale Deep understanding of container orchestration and distributed systems architecture High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies) Experience managing GPU clusters and optimizing compute resource utilization Required Skills Expert-level Kubernetes administration and YAML configuration management Proficiency with Slurm job scheduling, resource mana

We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS. As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters Responsibilities Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads Manage and optimize Slurm-based HPC environments for distributed training of large language models Develop robust APIs and orchestration systems for both training pipelines and inference services Implement resource scheduling and job management systems across heterogeneous compute environments Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands Qualifications Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization Experience with deploying and managing distributed training systems at scale Deep understanding of container orchestration and distributed systems architecture High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies) Experience managing GPU clusters and optimizing compute resource utilization Required Skills Expert-level Kubernetes administration and YAML configuration management Proficiency with Slurm job scheduling, resource manag

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, Seattle, Washington D.C., Raleigh, London, and Amsterdam. The Data team within Plaid’s Fraud organization builds the machine learning systems that power Plaid’s fraud detection products, leveraging Plaid’s unique network data to identify and stop fraud before it happens. The team owns the full ML lifecycle—from feature pipelines and model training to production serving and monitoring—building reliable, scalable systems that deliver high-quality fraud detection as we grow to support hundreds of customers. As a Senior Machine Learning Engineer, you will own the development of high-performance feature computation and online inference pipelines that power production machine learning systems at scale. You’ll build robust observability, monitoring, and automated debugging capabilities, while leveraging AI-assisted tools to investigate complex system behavior and maintain high reliability. You’ll partner closely with ML Infrastructure, Data Science, and Product teams to execute critical technical initiatives and deliver scalable, high-impact ML solutions. Responsibilities: Build and scale machine learning systems that power a rapidly growing fraud detection product in a fast-paced environment. Solve complex technical challenges at the intersect