About the job
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.
Responsibilities
- 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
- Work across the Anyscale Workspaces stack to improve the path from local code to distributed execution, including environments, dependencies, images, authentication, workload submission, and debugging
- Build cohesive experience, tools and frameworks for the AI development lifecycle, including data preparation, fine-tuning and post-training, evaluation, production serving, dataset management, experiment tracking, and lineage
- Build the path from a trained model to a reliable production endpoint, including model registration, deployment workflows, performance benchmarking, and LLM-specific service metrics
- Surface observability across the CLI, SDK, UI, and agent-facing interfaces so users can diagnose failures across jobs, tasks, actors, nodes, and GPUs
Qualifications
Minimum
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
- 5+ years of experience writing high-quality production code
- A 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 that people choose to use
Preferred
- 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