Level

Adaption

Inference Engineer

AI in this role

Own and optimize the inference stack, improving throughput, cost, and latency for serving AI models at scale.

vllmsglangtensorrt-llmpythoncrustcudanccl
ml-systemsinference-infrastructureperformance-engineeringmodel-servingquantization

The role

You'll own the cost and performance of our inference stack. Your work will determine how efficiently we serve models as workloads, traffic, and hardware change.

You'll work closely with the engineers operating the serving fleet while owning the core performance levers: caching, batching, quantization, decoding, and kernel-level optimization. Success means improving throughput and latency without compromising reliability or model quality.

Responsibilities

  • Improve throughput, cost, and tail latency through KV-cache management, continuous batching, speculative decoding, and quantization.

  • Optimize long-context prefill and decode workloads based on real production traffic.

  • Tune routing between our infrastructure and external providers based on cost, capacity, and performance.

  • Work within serving engines such as vLLM, SGLang, and TensorRT-LLM, going below the framework when needed.

  • Build profiling and measurement systems that show where time, memory, and compute are being spent.

Qualifications

  • 5+ years in ML systems, inference infrastructure, or performance engineering, with measurable improvements in cost or latency.

  • Deep understanding of model serving, including prefill and decode, memory bandwidth, batching, and concurrency.

  • Production experience with serving engines such as vLLM, SGLang, or TensorRT-LLM.

  • Strong Python skills and proficiency in C++, Rust, or another systems language.

  • Experience with GPU performance, including CUDA, NCCL, mixed precision, memory layout, kernels, or quantization.

Above all, we're looking for great teammates who make work feel lighter and aren't afraid to go out on a limb with bold ideas. You don't need to be perfect, but you do need to be adaptable. We encourage you to apply, even if you don't check every box.

 

About us

Most AI is frozen in place - it doesn't adapt to the world. We think that's backwards. Our mandate is to build efficient intelligence that evolves in real-time. Our vision is AI systems that are flexible, personalized, and accessible to everyone. We believe efficiency is what makes this possible - it's how we expand access and ensure innovation benefits the many, not the few. We believe in talent density: bringing together the best and most driven individuals to push the boundaries of continual adaptation. We're looking for builders and creative thinkers ready to shape the next era of intelligence.

 

Benefits

  • Flexible work: In-person collaboration in the Bay Area, a distributed global-first team, and team offsites.

  • Adaption Passport: Annual travel stipend to explore a country you've never visited. We're building intelligence that evolves alongside you, so we encourage you to keep expanding your horizons.

  • Lunch Stipend: Weekly meal allowance for take-out or grocery delivery.

  • Well-Being: Comprehensive medical benefits and generous paid time off.

How we rate this

Inference Engineer at Adaption rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

ML SystemsInference InfrastructurePerformance EngineeringModel ServingQuantizationvLLMSglangTensorrt LLM

Questions you could be asked

  1. Tell me about a project where ml systems was part of your work. What did you do?
  2. Tell me about a project where inference infrastructure was part of your work. What did you do?
  3. Tell me about a project where performance engineering was part of your work. What did you do?
  4. Tell me about a project where model serving was part of your work. What did you do?
  5. Tell me about a project where quantization was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: ML Systems, Inference Infrastructure, Performance Engineering, Model Serving, and Quantization. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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