Software Engineer Ml Infrastructure Jobs

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 Ray Data Team: Ray Data is Python-native data processing engine that is a one stop shop for all AI data processing needs. Ray Data provides performant, first-class integration with cutting edge AI frameworks using both multi-modal and structured data. The Ray Data team currently develops and maintains Ray Data . We are a team of engineers passionate about building a Data processing engine which is a one-stop shop for all of your ML/AI needs. We are looking for exceptional engineers to build, optimize, and scale Ray for modern and increasingly complex AI workloads. As part of this role, you will: Improve the performance of Ray Data and multi-modal batch inference use cases. Ensure efficient scaling across different stages of the Data pipeline in a heterogeneous environment. Building data loading solutions for production training workloads. Focus on stability and fault tolerance at high scale Working with customers and new age AI native companies in scaling their AI workloads. We'd love to hear from you if have: At least 3-4 years of relevant work experience Solid background in building scalable and fault-tolerant distributed systems Experience with data processing, database internals. Passionate about large

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. Marketplace teams are at the heart of our products and decision-making, owning everything from rider pricing to driver earnings, incentives, and efficient matching. We’re looking for passionate, driven engineers to build systems that empower our riders and drivers to have the best transportation experience possible through prediction, adaptivity, and personalization. We’re looking for someone who is excited about working in a fast-paced, innovative, and impactful environment to create reliable solutions to distributed computing, ML, and data problems. The Pricing team is a centerpiece of Lyft’s Marketplace org, determining prices for all rideshare products and supporting new initiatives. Rider Engagement develops rider-facing engagement levers and optimizes user pricing experience to drive both short term and long term business outcomes. We work with Product & Science to solve and implement complex pricing requirements, balancing the needs of riders, drivers, and the business goals. As an owner of one of the most critical flows in the company, you will work on a wide array of challenges such as latency-sensitive concurrency problems, large scale distributed systems, and experimentation. If you’re interested in playing a large part in demand / supply management and improving the Lyft customer experience, this could be a great fit for you. Responsibilities: Drive high-impact projects and innovate new solutions to provide the best user experience. Work closely with cross-functional teams and partner teams to develop solutions based on technology and business needs, and advance team’s goals and priorities Independently lead features from idea to positive execution and launch Unblock, support and communicate with internal partners to achieve results Write well-crafted, well-tested, readable, maintaina

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. Marketplace teams are at the heart of our products and decision-making, owning everything from rider pricing to driver earnings, incentives, and efficient matching. We’re looking for passionate, driven engineers to build systems that empower our riders and drivers to have the best transportation experience possible through prediction, adaptivity, and personalization. We’re looking for someone who is excited about working in a fast-paced, innovative, and impactful environment to create reliable solutions to distributed computing, ML, and data problems. The Pricing team is a centerpiece of Lyft’s Marketplace org, determining prices for all rideshare products and supporting new initiatives. Rider Engagement develops rider-facing engagement levers and optimizes user pricing experience to drive both short term and long term business outcomes. We work with Product & Science to solve and implement complex pricing requirements, balancing the needs of riders, drivers, and the business goals. As an owner of one of the most critical flows in the company, you will work on a wide array of challenges such as latency-sensitive concurrency problems, large scale distributed systems, and experimentation. If you’re interested in playing a large part in demand / supply management and improving the Lyft customer experience, this could be a great fit for you. Responsibilities: Drive high-impact projects and innovate new solutions to provide the best user experience. Work closely with cross-functional teams and partner teams to develop solutions based on technology and business needs, and advance team’s goals and priorities Independently lead features from idea to positive execution and launch Unblock, support and communicate with internal partners to achieve results Write well-crafted, well-tested, readable, maintaina

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 (e.g. a machine learning pipeline of feature engineering, model training, and evaluation). Data is usually a core element connecting these different stages, and therefore plays a critical role in Ray’s usability, performance, and stability. We are looking for strong engineers to build, optimize, and scale Ray’s Datasets library and data processing capabilities in general. About the Ray Data team: The Ray Data team currently develops and maintains the Ray Datasets library, which is already powering critical production use cases (e.g. large scale data compaction at Amazon , and ML pipeline at Alibaba ). Ray Datasets is a Python library built on top of Apache Arrow and Ray Core (Ray’s C++ backend), and the Ray Data team interacts closely with Ray Core components including the scheduler and the memory & I/O subsystems. The Ray Data team also works closely with Ray’s ML libraries including Train, RLlib, and Serve. A snapshot of projects you will work on: - Performance of Ray Datasets at large scale (leveraging Arrow primitives, optimizing Ray object manager, etc.) - Integration with ML training and data sources - Stability an

The Core Data Science team builds the products and data assets that let teams across Zoox measure and improve the autonomy and operational performance of our fleet. In this role, you'll focus on fleet simulation — the environment our data teams use to develop, test, and evaluate the algorithms that orchestrate our autonomous fleet (dispatch, routing, and vehicle assignment) and to understand how those algorithms perform before they reach the road. You’ll piece together the constituent components into a coherent, easy-to-use simulation environment, filling in the gaps, and building generalizable mechanisms so data scientists can measure their algorithm's impact on fleet efficiency. You'll join a diverse, experienced team with rapidly growing scope while working with one of the industry's most valuable autonomous vehicle datasets. In this role, you will: Turn existing fleet-simulation components into a coherent, self-service environment that data scientists and ML engineers can run without deep software expertise. Fill gaps and build the connective tissue between simulation, data, and fleet-orchestration algorithms. Build generalizable mechanisms that let teams measure how their orchestration algorithms affect fleet efficiency. Collaborate with data science, ML, and fleet-orchestration teams to shape and prioritize the simulation roadmap. Establish best practices and processes around simulation, evaluation, and data-driven decision-making. Qualifications Software development experience in a production environment, with fluency in Python; or demonstrated experience learning new programming languages. Experience building tools, services, or frameworks that make complex systems usable by non-specialist users. Familiarity with any version control system, with preference for Git. Experience designing metrics and building mechanisms that deliver robust, actionable insights. Good communi

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 Ray Data Team: Ray Data is Python-native data processing engine that is a one stop shop for all AI data processing needs. Ray Data provides performant, first-class integration with cutting edge AI frameworks using both multi-modal and structured data. The Ray Data team currently develops and maintains Ray Data . We are a team of engineers passionate about building a Data processing engine which is a one-stop shop for all of your ML/AI needs. We are looking for exceptional engineers to build, optimize, and scale Ray for modern and increasingly complex AI workloads. As part of this role, you will: Improve the performance of Ray Data and multi-modal batch inference use cases. Ensure efficient scaling across different stages of the Data pipeline in a heterogeneous environment. Building data loading solutions for production training workloads. Focus on stability and fault tolerance at high scale Working with customers and new age AI native companies in scaling their AI workloads. We'd love to hear from you if have: At least 3-4 years of relevant work experience Solid background in building scalable and fault-tolerant distributed systems Experience with data processing, database internals. Passionate about large

About the Team The Spark Platform team owns and operates DoorDash's Apache Spark ecosystem — the execution runtime, remote shuffle service, cluster scheduler, and reliability tooling that powers the company's data, analytics, and ML workloads. We run Spark across the company at significant scale and continue to expand the workloads, capabilities, and consumer base we serve. Orchestrating and operating thousands of Spark cluster deployments is a complex distributed system problem which the team invests heavily in runtime optimization, systems architecture, multi-tenant scheduling, and end-user tooling. About the Role As a Software Engineer on Spark Platform, you will execute across the surfaces of our in-house Spark deployment that serves the entire company. The work spans Spark runtime upgrades and performance, multi-tenant scheduling and executor bin-packing on Kubernetes, cluster lifecycle automation, and the observability and incident automation that keep the platform sustainable. You will move between layers as the work demands — picking up the next high-leverage problem regardless of where it sits — and partner closely with the rest of the team and with platform consumers across the company. You must be located in San Francisco, Sunnyvale, Seattle, or New York City for this hybrid position. You will report into the Engineering Manager on our Spark Platform team. You're excited about this opportunity because you will… Build and operate an in-house Spark platform that runs at company-wide scale, spanning runtime, scheduler, reliability, and user-facing tooling. Drive multi-tenant scheduling, executor bin-packing, and cost-aware placement that let a small team serve dozens of consumer teams. Own pieces of cluster lifecycle automation — provisioning, upgrades, capacity changes, and node-failure handling — at a scale where these stop being manual events. Build the observability and incident automation that make the platform debuggable end-to-end and keep on-call sus

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. The Customer Experience Engineering Team builds the internal and external technologies that scale Snowflake’s global support and sales organizations. We empower our technical experts by providing the advanced tools they need to resolve complex issues and drive customer success. Our team specializes in software engineering, data-driven decisions, ML, and LLM-based solutions . We build production-grade systems to automate manual processes and augment the capabilities of our technical staff. Our current focus includes: LLMs : Developing and deploying LLM and agent-based architectures for streamlining troubleshooting Scalable Evaluations : Implementing large-scale evaluations to ensure the quality and reliability of our internal and external tools Process Automation : Designing intelligent workflows that eliminate bottlenecks and allow our experts to focus on the most technical aspects of the Snowflake platform Incident discovery: using embeddings, LLMs, clustering, and agents to detect potential widespread issues more quickly Now, the team is growing, and we are looking for a Software Engineer to join us. In this role, you will work closely with the state of the art LLM models, fine-tune them, develop agents, apply various clusterings, summarizations, embeddings, and so on. Ev

Truveta is the world’s first health provider led data platform with a vision of Saving Lives with Data. Our mission is to enable researchers to find cures faster, empower every clinician to be an expert, and help families make the most informed decisions about their care. Achieving Truveta’ s ambitious vision requires an incredible team of talented and inspired people with a special combination of health, software and big data experience who share our company values . Truveta was born in the Pacific Northwest in the USA and has employees who live across the country. As we expand our global footprint with a new presence in Hyderabad, India, we’re building a world-class team to help us scale this mission and Clinical Intelligence and AI Evaluation is at the heart of everything we do. This job requires daily onsite work at our office in Hyderabad, India. This Opportunity Join the founding team of the Truveta India Development Center (IDC) and play a pivotal role in shaping its future. As one of the initial members, you'll help build and scale a high-impact organization while contributing to products and platforms that advance healthcare through data and AI. This is a unique opportunity to influence both the technical direction and the culture of a growing global engineering hub. This highly visible position will report to our Director of Clinical Intelligence and AI Evaluation team directly and will collaborate closely with engineers, product managers, designers, and ML teams across geographies. Who We Need Truveta is rapidly building a talented and diverse team to tackle complex healthcare domain data and technical challenges. Beyond core capabilities, we are seeking problem solvers, passionate and collaborative teammates, and those willing to roll up their sleeves while making a difference. If you are interested in the opportunity to pursue purposeful work, join a mission-driven team, and build a rewarding career while having

Be the visionary Perception Architect designing the sensory "nervous system" for our next generation of autonomous products. Evolve our end-to-end perception architecture, serving as the essential liaison between Sensing teams and the core ML system. Drive architectural evolution toward next-generation sensing and compute platforms by influencing Hardware-Software Co-Design. In this role, you will: System Synthesis: Define the end-to-end perception architecture—assess new sensing (LiDAR, Radar, Camera) to the final fused world model. Hardware-Software Co-Design: Partner with Compute and Sensing teams to influence the design, ensuring our algorithms have the "computational oxygen" they need. Algorithmic Strategy: Lead the transition from current pipelines to AI innovation pipelines, while maintaining the safety and interpretability required for edge deployment. Future-Proofing: Evaluate emerging technologies and determine their viability for the 3-5 year roadmap. Performance Budgeting: Collaborate on rigorous metrics for latency and accuracy across diverse edge-computing environments. Lead cross functional teams: to create a viable plan to move to new platforms and unlock further technical capabilities for Perception systems. Coordinate projects across partner teams to make sure work is aligned with the overall org direction. Qualifications Education: Ph.D. or MS in Computer Science, Robotics, Electrical Engineering, or a related field with a focus on Computer Vision . Experience: 8+ years in autonomous systems with at least 2 years in an architectural role , shipping and evolving perception systems Deep Domain Expertise: Mastery of systems focused on Computer Vision, De

Scale GP (Scale Generative AI Platform) is an enterprise-grade Generative AI platform that provides APIs for knowledge retrieval, inference, evaluation, and more. We are looking for a strong engineer to join our team and help us build and scale our product in a fast-paced environment. The ideal candidate will have a strong understanding of software engineering principles and practices, as well as experience with large-scale distributed systems. You will be responsible for owning large new areas within our product, working across backend, frontend, and interacting with LLMs and ML models. You will solve hard engineering problems in scalability and reliability. You will: Own large new areas within our product Work across backend, frontend, and interacting with LLMs and ML models Deliver experiments at a high velocity and level of quality to engage our customers Work across the entire product lifecycle from conceptualization through production Be able, and willing, to multi-task and learn new technologies quickly Ideally you'd have: 4+ years of full-time engineering experience, post-graduation Experience scaling products at hyper growth startups Experience tinkering with or productizing LLMs, vector databases, and the other latest AI technologies Proficient in Python or Javascript/Typescript, and SQL Experience with Kubernetes Experience with major cloud providers (AWS, Azure, GCP) Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval

About the Team The Workload Networking team is responsible for the collective communication stack used in our largest training jobs. Using a combination of C++ and CUDA we work on novel collective communication techniques that enable efficient training of our flagship models on our largest custom built supercomputers. The models we train are key ingredients to the AI research progress at OpenAI and the field as a whole, and we continually incorporate learnings from our entire research org into our training platform. About the Role As a Software Engineer, Networking you will design and implement custom networking collectives that are tightly integrated into our training stack. We’re looking for people who have a background in low level performance critical software. Experience with collective communication is a bonus. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Collaborate closely with ML researchers to design and implement efficient collective operations in C++ and CUDA. Ensure that our largest training jobs take full advantage of the different network transports used in our supercomputers. Work on simulations to inform our future supercomputer network designs. You might thrive in this role if you: Have written distributed algorithms using RDMA in the past. Are comfortable writing low level performance sensitive CPU and/or GPU code. Are familiar with network simulation techniques. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voic

At Freddie Mac, our mission of Making Home Possible is what motivates us, and it’s at the core of everything we do. Since our charter in 1970, we have made home possible for more than 90 million families across the country. Join an organization where your work contributes to a greater purpose. Position Overview: Are you looking to be part of a technology team beyond the ordinary? A place full of exciting challenges and the engineers up to solving them? Then welcome to Financial Engineering! This is the place where the models become software, where many of the numbers that drive our business decisions are produced. Our systems are used for interest rate and credit risk management, investment analysis and trading, security issuance, automated loan underwriting, distressed loan decisions, economic and regulatory capital, property valuation, financial reporting, economic and GAAP forecasting, and more! Our Impact: Model Application Development team is responsible for converting models to application software. We evaluate millions of loans per day using the latest ML algorithms. We turn ML algorithms to APIs using web services. We are responsible for implementing underwriting, servicing and loss mitigation models Your Impact : Automating and deploying Financial Models developed using ML algorithms. Maintaining underwriting models, credit data processing. Deploying model configuration with market and economic data for loss mitigation models. Providing APIs to Financial Models to integrate with IT applications. Performance tuning and stress testing Models. Participating in model tie outs. Identifying and resolving any issues in reconciling with model results. Fixing software security vulnerabilities identified in SAST and FOSS scanning. Code review and guiding junior level developers. <u

About the team The Monetization team is a new cross-functional group spanning engineering, product, research, and design. We are building the foundational systems that will help OpenAI scale access to intelligence responsibly. Our mission is to create user-first, privacy-preserving monetization products—including next-generation ads experiences—that strengthen trust, expand economic opportunity, and support OpenAI’s long-term innovation. We believe monetization should deliver clear user value, uphold rigorous privacy and safety standards, and sustain a healthy ecosystem for developers and businesses. The team operates in a greenfield environment, moving quickly through prototyping, experimentation, and iterative deployment. We partner closely across Product, Design, and Research to bring new capabilities into real-world systems at global scale. About the role We’re looking for an experienced Software Engineer to lead the design and development of the systems that turn advertiser feeds (eg. retail feeds, travel feeds etc.) into relevant, reliably delivered ads. In this foundational role, you will build across the full ads lifecycle—from ingesting and organizing retailer or travel catalogs into efficient retrieval systems, supporting ranking models, and serving ads at scale. This role is well suited to an engineer with direct experience in product ads retrieval, ranking, and delivery, along with a strong understanding of merchant feeds and shopping systems such as Google Shopping, YouTube product ads, or Amazon. We also welcome engineers with Senior Staff-level or greater scope who have deep ownership of large-scale catalogs, search and indexing, ads serving, or closely related distributed systems. You will independently own complex systems, make sound technical tradeoffs, and help define the architecture and roadmap for Shopping/Travel/Feed Ads. You will collaborate closely with Ads ML, Ads Delivery, Product, Design, and Research to connect accurate, fresh advertiser

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. Build agents that learn from the work they do. Ema builds AI employees that carry out complex workflows across enterprise applications. Our ML team works on the loop that makes them better: production traces become data, data becomes training and evaluation, and better agents produce better traces. The hard part is deciding which intervention will improve behavior in the next real workflow. The problem space Harnesses and inference-time compute. Design context, tools, skills and orchestration for multi-step agents, including work across documents, slides, images, audio and video. Test where extra reasoning, search or verification earns its latency and cost. Build self-improvement loops with explicit permissions, evaluation gates and rollback. Agent post-training. Curate trajectories for SFT, optimize preferences, or run RL on real agent tasks. Investigate methods such as DPO, GRPO or DAPO where they fit; compare process and outcome supervision, shape rewards, and distill useful frontier behavior into smaller models. Measure whether gains transfer beyond the training environment. Environments and rewards. Turn enterprise workflows into reproducible training