Software Engineer, ML Developer Experience
Software Engineer on the ML Developer Experience team building developer tooling, SDKs, CLIs, APIs, and platform services for the Anyscale/Ray distributed computing platform, spanning cloud infrastructure to the Ray runtime.
- Compensation
- $226K–$283K / yr
- Location
- San Francisco
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Software engineering
- Posted
Skills
Must have
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
- 5+ years of experience writing high-quality production code
- Solid background in algorithms, data structures, and system design
- Experience working with modern machine learning tooling — PyTorch, MLflow, data catalogs, and similar
- Hands-on experience building and operating highly available services in production
- Strong product instincts and a track record of shipping developer-facing tools
Nice to have
- Experience building and maintaining open-source projects
- Experience building and operating machine learning infrastructure in production
- Experience building highly available serving systems
- Experience using Ray
Full description
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
Anyscale is looking for a Software Engineer to join the ML Developer Experience (MLDevX) team. MLDevX owns the experience layer of the Anyscale platform: the interfaces through which users and coding agents discover, configure, run, observe, debug, and productionize AI workloads. Every user journey crosses this layer through the CLI, SDKs, APIs, UI, Workspaces, MCP, or the workflows and integrations built on top of them. Together, these form the user’s primary interface into Anyscale, turning distributed computing from a systems problem back into a coding problem. We build the common contracts, tools, control-plane services, and architecture that power these surfaces.
You will work across the stack from developer tooling to cloud infrastructure and the Ray runtime. Manage long-running operations and make failures across jobs, tasks, actors, nodes, and GPUs easier to diagnose. The systems you build must scale with the platform, remain predictable through failures, and be intuitive for developers, programmable for applications, and operable by coding agents.
This is a high impact individual-contributor role with end-to-end ownership. You will work directly with users and field teams to identify high leverage problems, shape the product and technical design, and build and operate the solution. The scope spans the AI workload loop - data preparation, fine-tuning and post-training, serving, evaluation, and iteration as well as the developer loop: code, submit, monitor, debug, optimize, and re-submit. We are looking for someone with strong product judgment, a willingness to understand the user base, and the technical depth to build high quality software for everyone from a developer learning Ray for the first time to an AI-native company or enterprise running production workloads at scale.
A snapshot of projects you may work on
- Build the next generation of developer tooling and MLOps capabilities on Ray, designed for both developers and coding agents.
- Develop an agent-first CLI and cohesive SDK, API, and MCP surfaces with self-discovery, structured errors, dry-run support, and consistent behavior across platform resources.