Software Engineer, Model Serving Infrastructure

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 actively seeking talented engineers to join our team and contribute to the development of next-generation, high-performance machine learning serving systems. We value diversity and inclusion, and we encourage individuals from underrepresented groups to apply.

Many existing ML serving tools are inherited from previous infrastructure generations, but emerging ML applications present new requirements, such as high compute demands, specialized hardware needs, and the integration of multiple models and business logic within a single request. At Anyscale, our mission is to provide a powerful yet simple set of tools that enable the seamless deployment of complex ML applications in production.

The Challenge:

What if you could build the infrastructure that powers AI applications for millions of users worldwide? Ray Serve is the production-grade serving framework that makes this possible—and we need exceptional engineers to push its boundaries.

You'll be working on problems that sit at the intersection of distributed systems, machine learning, and high-performance computing. This isn't about maintaining CRUD apps or tweaking configurations—this is about solving fundamental computer science problems that directly impact how the world deploys AI.

What You'll Actually Build:

Example projects:

• Asynchronous inference: Let the client submit a request and get a request handle that asks for its requests completion while not blocking the client side. Really important for image, video, or audio generation applications.

• Sub-millisecond Model Routing: Design and implement intelligent request routing systems that dynamically balance load across thousands of model replicas while maintaining strict latency SLAs

• Zero-Downtime Model Updates: Build sophisticated traffic management systems that seamlessly transition between model versions at scale, handling terabytes of inference requests without dropping a single query

• State Management at Scale: With many models and many replicas deployed into production, the control loop’s state management can become the bottleneck for events such as routing, autoscaling, etc. What are the architectural improvements that can shift the envelop of scale by 10x going from 1000s replicas to 10,000s replicas, etc.

• Multi-Model Orchestration: Architect frameworks for complex ML pipelines where dozens of models need to communicate, share resources, and maintain end-to-end latency guarantees

• Observability & Debugging: Build deep introspection tools that make it trivial to debug distributed ML applications—because "works on my laptop" doesn't cut it at scale

The Tech You'll Work With:

• Deep Systems Programming: You'll write performance-critical code in Python (with Cython optimization paths) and potentially C++ for the hot paths

• Distributed Systems at Scale: Work directly with Ray Core's actor system, gRPC, and custom networking protocols to handle millions of requests per second

• Cloud-Native Infrastructure: Kubernetes, service meshes, and custom operators—you'll need to understand and extend the cloud native ecosystem

• ML/AI Systems: TensorFlow, PyTorch, JAX, transformers—you don't need to be an ML expert, but you'll develop deep system-level knowledge of how these frameworks work under the hood

• Production Reliability: OpenTelemetry, Prometheus, distributed tracing, and chaos engineering to ensure 99.99% uptime. Availability and performance are our key objectives as a serving infrastructure.

• Using AI coding agents: We are an AI-forward company, leveraging coding agents to scale our-selves while keeping the team lean and highly utilized.

What We're Looking For:

Must-Haves

• Strong Systems Fundamentals: You understand operating systems, networking, concurrency, and distributed systems at a deep level and the trade-offs that different design options imply

• Production Experience: You've built and maintained systems that serve real users at scale

• Code Quality: Have a good taste in code quality, simplicity, generality, testing coverage. AI-agents write a lot of code in short time, you should be able to instruct them to output what is golden standard

• Ownership Mindset: You take responsibility for your code in production—from design to deployment to incident response

Nice-to-Haves

• Experience with distributed systems frameworks (gRPC, Ray)

• Background in ML/AI systems or serving infrastructure

• Contributions to major open source projects

• Experience with performance optimization and profiling

• Knowledge of cloud-native technologies (Kubernetes, Istio, etc.)

What Really Matters

We care more about how you think and solve problems and whether you have shown patterns of end to end ownership in your past stages of career than checking boxes. If you're intellectual