Software Engineer- Inference

Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, Open Evidence, 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.

We're looking for inference performance engineers who want to make the world's most demanding AI workloads run faster and more efficiently. You'll work across the stack, from the inference engine and runtime through scheduling, serving, and routing. Along the way you'll apply techniques like prefill/decode disaggregation, speculative decoding, and KV-cache management. You'll reason from first principles about where time and memory go, find what's holding performance back, and close the gap.

Your work directly impacts how fast our customers' models run and how efficiently we serve them. This role is ideal for someone who thrives in a fast-paced startup environment and is eager to make significant contributions to the exciting field of LLM inference.

You'll get to work on these types of projects as an Inference Performance engineer:

Agentic Kernels in Production How we built the new fastest API for GLM-5.2

Live draft model training for speculative decoding

Making Kimi K3 Tokenization 18x faster

The Baseten Inference Stack Driving model performance optimization

Implement and product ionize cutting-edge inference techniques, working deep in runtime internals. That includes quantization, speculative decoding, KV-cache reuse, chunked prefill, LoRA, guided generation for structured outputs, and custom scheduling and routing algorithms.

Profile and optimize inference end to end, from kernel launch overhead and memory layout up to request scheduling, prefill/decode disaggregation, and cache-aware routing. Run cross-layer investigations, such as tracing a tail-latency regression from request timing through routing and batching down to a kernel.

Turn performance into cost savings. Improve tokens per GPU-hour, raise utilization, and give customers and internal teams clear latency/throughput/cost tradeoffs.

Bring up and tune new model architectures on new hardware quickly, often in the same week they're released.

Build benchmarking frameworks that measure real-world performance across model architectures, batch sizes, sequence lengths, and hardware configurations.

Contribute upstream to open-source inference engines (vLLM, SGLang, TensorRT-LLM), and partner closely with model, infrastructure, and customer-facing teams to ship wins.

Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, Mathematics, or related field.

Experience with one or more general-purpose