Staff / Principal Machine Learning Engineer, Serving - USA

Inworld; Mountain View (CA); Hybrid; USD 270,000 - 500,000; Full time

Benefits offered by this job

Relocation assistance

Equity options

Comprehensive benefits

Job summary

A tech company in Mountain View seeks talented engineers for a role emphasizing high-performance systems, inference optimization, and model acceleration. You will thrive in ambiguity, tackle unclear problems, and design impactful solutions. The position offers a competitive base salary between $270,000 and $500,000, along with bonuses, equity, and benefits. Ideal candidates hold a relevant PhD or equivalent experience and have a knack for full-cycle ownership in engineering projects. Join a dynamic team prioritizing performance and collaboration.

Qualifications

• Deep understanding of inference optimization frameworks.
• Hands-on experience with model acceleration techniques.
• Proficiency in C++, CUDA, Rust, or optimized Python.
• Experience with Kubernetes and multi-GPU inference.
• Track record of impactful public work in systems programming.
• Ability to own models from research to production.

Responsibilities

• Transform unclear problems into clear solutions.
• Prioritize impact over theoretical optimizations.
• Ensure stability and performance before launch.
• Foster collaboration and strong team culture.

Skills

Inference Optimization

Model Acceleration

High-Performance Systems

Distributed Systems & Scaling

Public work

Full-cycle ownership

PhD in CS/Physics/Math

Education

PhD in CS, Physics, Math, or equivalent experience

Job description

Who We're Looking For

A year ago, reliably working agentic systems and sub-second multimodal inference at scale barely existed. Nobody has a decade of experience here. So we're not screening for a resume template — we're looking for strong people from varied backgrounds who learn fast, thrive in ambiguity, and can show us what they've built, broken, and understood.

Experience We Find Useful

You don't need all of this. But you need enough to make a case.

• Inference Optimization. Deep understanding of modern serving frameworks and techniques like vLLM or TRT-LLM.
• Model Acceleration. Hands-on experience with quantization, distillation, caching strategies, continuous batching, paged attention, and speculative decoding.
• High-Performance Systems. Proficiency in C++, CUDA, Rust, or highly optimized Python. You know how to profile code and squeeze every ounce of performance out of NVIDIA GPUs.
• Distributed Systems & Scaling. Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and reliably handling thousands of concurrent connections.
• Public work. Non-trivial systems programming projects, open-source contributions to major inference engines, or deep-dive technical write-ups.
• Full-cycle ownership. You can take a model from the research team, containerize it, optimize its serving, and ensure it runs reliably in production.
• Background. PhD in CS, Physics, Math, or equivalent practical experience building backend or ML systems.

Who Thrives Here

• You don’t need a roadmap to start walking; you’re comfortable picking a direction and building the map as you go.
• You believe engineering isn't finished until it’s shipped and stable. You have a bias for impact over purely theoretical optimizations.
• You don't just ship code; you obsess over the why. You’re the first to question an architecture if you think there’s a better way to solve the core latency or throughput problem.
• You aren't satisfied with "the PM said so." You thrive on deep context and want to understand the fundamental logic behind every decision we make.

What Working Here Is Like

We hand you unclear problems and expect you to make them clear. We value engineers who say "I don't know yet" and then design the benchmark or prototype that finds out. We treat performance, latency, and reliability as first-class product features, not a box to check before launch. Impact comes before everything else, though we support sharing work and open-source contributions that move the field forward. Your work should be visible. Flat structure, fast iterations, minimal process theater.

We believe in the power of in-person collaboration to solve the hardest problems and foster a strong team culture. We offer relocation assistance and look forward to you joining us in our Mountain View office.

Benefits & Compensation

The base salary range for this full-time position is $270,000 - $500,000, plus bonus, equity, and benefits.