Sobre el puesto
ABOUT BASETEN
Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products.
THE ROLE
We're looking for distributed systems engineers and product-minded generalists to build the distributed runtime that powers large-scale LLM inference on Baseten. Our inference platform empowers customers to deploy and operate cutting-edge models with industry-leading performance, scalability, and reliability. It also powers Model APIs, our hosted endpoints for the latest open-source models. You'll work across the stack, from the developer experience customers use to deploy models, through the libraries behind features like tool calling and reasoning, down to the systems that orchestrate deployments on Kubernetes and route traffic efficiently. Your job is to make sure every model on our platform is fast, reliable, and cost-efficient. You'll join a small, high-impact team at the intersection of distributed systems, model performance, infrastructure, and product, helping define how developers use AI models at scale. This role is ideal for engineers who enjoy owning systems in production, solving hard integration problems, and making complex infrastructure simple and reliable for users.
EXAMPLE INITIATIVES
You'll get to work on these types of projects on our Inference Platform:
• The Baseten Inference Stack at NVIDIA Dynamo Day
• 2x faster inference with KV cache-aware routing
• How Baseten multi-cloud capacity management (MCM) unifies deployments
RESPONSIBILITIES
• Build the infrastructure and orchestration systems that deploy and run large-scale distributed LLM inference, including routing, autoscaling, scheduling, and runtime management.
• Design, build, and operate Model APIs, with a focus on advanced inference capabilities: structured outputs (JSON mode, grammar-constrained generation), tool/function calling, and multimodal serving.
• Implement platform fundamentals such as API versioning, validation, usage metering, quotas, and authentication.
• Instrument deep observability (metrics, traces, logs) and build repeatable benchmarks for speed, reliability, and quality. Help set best practices for testing, release automation, and operational excellence.
• Debug and harden complex production systems spanning Kubernetes, distributed runtimes, networking, and GPU workloads to improve reliability and scalability.
• Partner with Inference Performance engineers and other teams to make new optimizations broadly available to customers and easy to configure.
• Own projects end to end, from architecture through deployment, monitoring, and iteration on customer feedback. Along the way, make thoughtful tradeoffs between performance, reliability, operational simplicity, and developer experience.
REQUIREMENTS
• Bachelor's, Master's, or Ph.D. in Computer Science, Engineering, or a related field, or equivalent practical experience.
• 3+ years building and operating distributed systems, backend infrastructure, or large-scale APIs where reliability, latency, and scale are first-class concerns.
• A proven track record of owning low-latency, reliable backend services, including rate limiting, auth, quotas, metering, and migrations.
• Infrastructure instincts with a feel for performance: profiling, tracing, capacity planning, and SLO management.
• Comfort debugging performance and reliability issues across multiple layers of the stack, from application behavior down to runtime and infrastructure internals.
• A strong sense of developer experience. You think about how systems are used, not just how they work.
• Eagerness to learn new languages, frameworks, and systems, and a real interest in inference engineering. Prior ML or LLM experience isn't required, though experience with model serving or inference systems is a plus.
• Excellent written communication and collaboration skills, including writing clear design docs and working across functions.
NICE TO HAVE
• Experience with or contributions to LLM inference engines and frameworks such as vLLM, SGLang, TensorRT-LLM, TGI, or Dynamo.
• Deep Kubernetes experience, including operators and custom resources, plus familiarity with service meshes or API gateways.
• Experience with distributed scheduling, autoscaling, or service orchestration.
• Experience operating GPU workloads in production.
• A background in developer-facing infrastructure or APIs, or contributions to open-source infrastructure or ML systems.
• Familiarity with observability tooling, CI/CD systems, or release automation.
BENEFITS
• Competitive compensation, including meaningful equity
• (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents
• Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)
• Paid parental leave
• Fertility and family-building stipend through Carrot
• (U.S. only) Company-facilitated 401(k)
• Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.
Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you.
At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.
We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).
Publicada el 4 oct 2026
