Senior Software Engineer, ML API – AI Inference

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.

Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.

About the Role Cerebras is building a new generation of disaggregated AI inference systems that combine GPU-accelerated prefill with ultra-fast decode on the Cerebras Wafer-Scale Engine.

We are hiring a Senior Software Engineer to build and evolve the ML API layer that makes this heterogeneous serving system accessible, reliable, and easy to use. You will own significant features and components across our inference APIs, model integration layer, and request-routing services, delivering a consistent experience across models and accelerator backends.

This role sits at the intersection of machine learning systems, API design, model serving, and distributed systems. You will translate evolving model capabilities and customer requirements into clear designs, lead implementation across multiple components, and carry features through validation, rollout, and production operation. Your work will help ensure that streaming, sampling, tool use, structured outputs, multimodal inputs, and model configuration behave correctly and consistently.

You will work closely with model enablement, compiler, runtime, cloud infrastructure, product, and customer-facing teams, as well as customers directly. This is a hands-on individual-contributor role with opportunities to lead small project teams, mentor engineers, and improve the quality and reliability of production inference software.

Responsibilities Own features end to end. Design, implement, test, and operate features for chat completions, text generation, streaming, model configuration, tool calling, structured outputs, multimodal inputs, and emerging inference capabilities.

Lead multi-component delivery. Write clear design documents, evaluate technical tradeoffs, break projects into deliverable milestones, and coordinate implementation with a small group of engineers and partner teams.

Deliver consistent API behavior. Implement clear request and response semantics across GPU prefill, Cerebras decode, and other inference backends. Maintain compatibility with widely adopted inference interfaces and evolve owned APIs through validation, versioning, and deprecation best practices.

Enable new models and capabilities. Integrate foundation models, tokenizers, chat templates, generation configuration, sampling methods, and model-specific features into the serving platform.

Integrate serving components. Extend inference services and their integration with frameworks and runtimes such as vLLM, PyTorch, Hugging Face libraries, LL Guidance, and Cerebras runtime components.

Support disaggregated inference. Implement routing, state-transfer integration, error handling, retries, cancellation, and request lifecycle management to coordinate GPU prefill with Cerebras decode.

Deliver measurable performance improvements. Diagnose bottlenecks and improve time to first token, streaming latency, throughput, and resource efficiency through changes to tokenization, serialization, batching, scheduling, and communication.

Own correctness and release quality. Build conformance tests, model-validation suites, numerical comparisons, integration tests, and performance benchmarks that catch regressions in owned features and support safe releases.

Strengthen production reliability. Improve service metrics, tracing, logging, alerts, and diagnostic tools. Investigate production incidents, resolve root causes, and follow through on improvements to resilience and incident response.

Improve customer and developer experience. Partner with product, support, and solutions teams to understand user needs and make practical tradeoffs. Improve configuration, documentation, examples, and debugging workflows.

Raise the engineering quality bar. Provide thoughtful design and code reviews, mentor less-experienced engineers, share domain knowledge, and…