RESPONSIBILITIES

• Implement and productionize 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.

REQUIREMENTS

• Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, Mathematics, or related field.
• Experience with one or more general-purpose programming languages, such as Python or C++.
• Familiarity with LLM optimization techniques (e.g., quantization, speculative decoding, continuous batching).
• Strong familiarity with ML libraries, especially PyTorch, TensorRT, or TensorRT-LLM.
• Demonstrated interest and experience in LLMs.
• Deep understanding of GPU architecture.

NICE TO HAVE

• Proficiency in enhancing the performance of software systems, particularly in the context of large language models (LLMs)
• Contributed to vLLM, SGLang, TensorRT-LLM, or another inference engine.
• Worked on large-scale distributed serving: autoscaling, load balancing, multi-region or multi-cloud capacity.
• Written or optimized GPU kernels (CUDA, Triton, CUTLASS, or similar)
• Worked on quantization (FP8/FP4, AWQ, GPTQ) or speculative decoding in production.
• Deep understanding of software engineering principles and a proven track record of developing and deploying AI/ML inference solutions.

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).

Compensation: $180K - $360K