Software Engineer- Inference Performance

Baseten
San Francisco +4 more
Hybrid

Who this role is best for

Candidates with advanced degrees in computer science and experience in LLM optimization will find this role with Baseten offers a chance to work on cutting-edge inference techniques across multiple geographies.

Best fit for

  • Individuals with a background in GPU architecture and optimization techniques for large language models
    — “Deep understanding of GPU architecture”
  • Engineers experienced in productionizing ML inference methods and contributing to open-source inference engines
    — “Contribute upstream to open-source inference engines (vLLM, SGLang, TensorRT-LLM)”
  • Professionals who can bridge performance improvements with cost savings and real-world benchmarking
    — “Turn performance into cost savings. Improve tokens per GPU-hour, raise utilization”

Things to consider

  • The role requires a strong grasp of multiple ML libraries and inference engines, including PyTorch and TensorRT-LLM
    — “Strong familiarity with ML libraries, especially PyTorch, TensorRT, or TensorRT-LLM”
  • Candidates must be prepared to work with new model architectures and hardware on short timelines
    — “Bring up and tune new model architectures on new hardware quickly”

How to stand out

  • Highlight experience with speculative decoding and kernel optimization in your resume and interview responses
    — “Apply techniques like prefill/decode disaggregation, speculative decoding”
  • Emphasize contributions to open-source inference engines or quantization work in production
    — “Worked on quantization (FP8/FP4, AWQ, GPTQ) or speculative decoding in production”
  • Showcase your ability to trace performance bottlenecks across layers of the system
    — “Run cross-layer investigations, such as tracing a tail-latency regression”
Pace · Fast PacedCollaboration · MediumAutonomy · HighDecision Impact · Company

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • Implementing cutting-edge inference techniques
  • Optimizing inference end-to-end
  • Building benchmarking frameworks
Typical background
Experience in inference performance optimization

Skills & requirements

Required

Inference Performance OptimizationProfiling And OptimizationBenchmarking FrameworksOpen-source Contribution

Preferred

LLM Inference Techniques

Stack & domain

Inference PerformanceLLM InferenceQuantizationSpeculative DecodingKv-cache ReuseChunked PrefillLoraGuided GenerationCustom SchedulingRouting AlgorithmsBenchmarking FrameworksOpen-source Inference EnginesProblem-solvingTeamworkCommunicationLeadershipInnovationAdaptabilityCollaborationAIPerformance OptimizationBenchmarkingOpen-source

About the role

Original posting from Baseten via Ashby

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 https://www.baseten.co/blog/announcing-our-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 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.

EXAMPLE INITIATIVES

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

  • Agentic Kernels in Production https://www.baseten.co/blog/agentic-kernels-in-production/
  • How we built the new fastest API for GLM-5.2 https://www.baseten.co/blog/how-we-built-the-new-fastest-api-for-glm-52/
  • Live draft model training for speculative decoding https://www.baseten.co/blog/live-draft-model-training-for-speculative-decoding/#eliminating-traditional-bottlenecks
  • Making Kimi K3 Tokenization 18x faster https://www.baseten.co/blog/making-kimi-k3-tokenization-18x-faster-for-million-token-agentic-workloads/
  • The Baseten Inference Stack https://www.baseten.co/resources/guide/the-baseten-inference-stack/
  • Driving model performance optimization https://www.baseten.co/blog/driving-model-performance-optimization-2024-highlights/

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).

Source: Baseten careers (Ashby)

Similar roles

More roles at Baseten