Software Engineer (Inference Platform)

• 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
• 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
• 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

Benefits

• Remote-first work environment. The Baseten team is welcome to work from wherever they want; fully remote, in our San Francisco office, or a mix of both. Today, our team (including our founding team) is spread across the United States, Canada, and Armenia. We provide a $1,000 stipend for you to make your home-office comfortable and productive
• Regular in-person team summits. We get together as a team three times a year to plan, workshop, and most importantly, get to know each other better
• Unlimited PTO. We ask that everyone take at least 4 weeks of vacation. And we have a company-wide break between Christmas and New Year’s Day
• Full healthcare coverage. Medical, dental and vision insurance for you and your family
• Paid parental leave. 16-weeks fully paid parental leave (adoptive and non-birth parents included) and flexibility with schedules while returning to work
• Company-sponsored 401(k) for you to contribute to
• Learning and development budget. We encourage you to take classes, attend conferences, and invest in your craft and we’ll cover expenses to make it happen- 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
• 3+ years building and operating distributed systems, backend infrastructure, or large-scale APIs where reliability, latency, and scale are first-class concerns
• Bachelor’s, Master’s, or Ph.D. in Computer Science, Engineering, or a related field, or equivalent practical experience
• Excellent written communication and collaboration skills, including writing clear design docs and working across functions
• A proven track record of owning low-latency, reliable backend services, including rate limiting, auth, quotas, metering, and migrations
• 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
• Comfort debugging performance and reliability issues across multiple layers of the stack, from application behavior down to runtime and infrastructure internals
• Infrastructure instincts with a feel for performance: profiling, tracing, capacity planning, and SLO management
• A strong sense of developer experience. You think about how systems are used, not just how they work
• 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