Senior ML Systems Engineer, Inference
Key Skills for This Role
Full Job Posting
Responsibilities
- Define how we measure inference performance, including throughput, time to first token, inter-token latency, and cost per token, and build the tooling that makes those measurements rigorous and repeatable.
- Profile and diagnose performance problems across the serving stack, from scheduling and memory management down to kernels and interconnect.
- Improve serving efficiency for large, state-of-the-art models on single-node and multi-node GPU deployments.
- Turn what you learn into production-ready runtimes, configurations, and defaults that customers benefit from automatically.
- Work closely with product and infrastructure teams to shape how inference is offered on Runpod.
- Keep up with the fast-moving inference ecosystem, including the open-source community, and decide what's worth adopting, what's worth building, and what's worth contributing back.
- Trace bottlenecks in the serving engine/runtime and implement fixes when configuration tuning is not enough.
Requirements
- 5+ years of professional system engineering experience.
- Deep, hands-on experience with vLLM, SGLang (or a comparable serving engine) in production or at serious benchmark scale.
- Strong software engineering skills in Python. You're comfortable working in large, performance-critical codebases.
- A solid understanding of what drives LLM inference performance: batching, memory, parallelism, and the trade-offs between latency and throughput.
- Experience with modern inference optimization techniques such as quantization, speculative decoding, or distributed serving.
- Rigor in benchmarking and performance analysis, plus comfort with GPU profiling tools.
- The ability to explain your results clearly in writing and turn them into decisions.
Preferred
Experience writing or tuning GPU kernels in CUDA or Triton.
Contributions to inference or ML systems projects.
Experience with multi-node GPU systems and high-speed networking.
Experience at a company where inference cost and latency were core business metrics.
What You’ll Receive:
The competitive base pay for this position ranges from ($150,000 - $220,000). This salary range may be inclusive of several career levels at Runpod and will be narrowed during the interview process based on a number of factors, including the candidate’s experience, qualifications, and location
Meaningful equity in a fast-growing company- everyone on the team receives stock options — your impact drives our growth, and you share in the upside.
Generous medical, dental & vision plans
Flexible PTO- take the time you need to recharge
Most roles are remote work first with an inclusive, collaborative teams utilizing slack as the main form of internal communication
Join a passionate team on the cutting edge of AI infrastructure — where culture, learning, and ownership are at the heart of how we scale.
$1,200 Home Office & Equipment Stipend- We set you up for success from day one with gear and support to create your ideal workspace
Runpod is committed to maintaining a workplace free from discrimination and upholding the principles of equality and respect for all individuals. We believe that diversity in all its forms enhances our team. As an equal opportunity employer, Runpod is committed to creating an inclusive workforce at every level. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, protected veteran status, disability status, or any other characteristic protected by law. We welcome every qualified candidate eligible to work in the United States; however, we are currently unable to sponsor employment visas.
About Runpod
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