Senior Machine Learning Engineer, Inference

For our client a fast-growing AI company building realtime voice and LLM technology we are looking for a Senior Machine Learning Engineer with strong expertise in ML inference optimization to join its inference team.

In this role you will own inference optimization end-to-end taking models from research to production at consumer scale and solving latency and throughput problems for realtime AI products used by hundreds of millions of people.

Our client is a well-funded venture-backed AI research lab and realtime inference provider with a small senior engineering team.

Inference: The team owns how our clients models run in production inference engines GPU efficiency model acceleration and the distributed serving infrastructure behind realtime voice and LLM products.

This is an opportunity for senior engineers who want to work deep inside modern inference engines on problems at real consumer scale.

• Own inference optimization end-to-end from research models to production deployment;
• Optimize latency and throughput of realtime voice and LLM inference at consumer scale;
• Work inside inference engines such as vLLM TRT-LLM and SGLang to improve performance;
• Apply model acceleration techniques quantization distillation speculative decoding caching continuous batching and paged attention;
• Write high-performance code in C CUDA Rust or optimized Python for GPU workloads;
• Build and scale distributed multi-GPU and multi-node serving with Kubernetes Ray and custom load balancing for high concurrency;
• Collaborate with research and product teams to bring new models into production.

• 5 years of experience as an ML inference or systems engineer at senior level (Google L5 / Meta E5 or equivalent);
• Hands-on ML inference optimization inside vLLM or TRT-LLM (engine-level work not only deployment);
• Experience building backend/ML systems and serving models in production;
• Systems programming and GPU optimization skills: C CUDA Rust or optimized Python;
• Experience with model acceleration: quantization distillation speculative decoding caching continuous batching paged attention;
• Distributed systems experience: Kubernetes Ray load balancing multi-GPU/multi-node inference high concurrency;
• Based in the US Canada UK Germany Switzerland or Serbia;
• Excellent communication skills in English.

• A track record of fast promotions;
• Public technical work open-source inference engine contributions or technical write-ups;
• PhD in Computer Science Physics or Mathematics or a degree from a top CS program;
• Ability to commute to Mountain View CA.

• Project/Client: Fast-growing AI company (realtime voice and LLM)
• Location: Onsite in Mountain View CA (preferred) or remote in the US Canada UK Germany Switzerland or Serbia
• Language: English
• Engagement: Full-time
• Contract: Permanent employment
• Salary: $250K$300K base competitive equity
• Visa and relocation: H-1B / OPT transfers supported; relocation assistance to the San Francisco Bay Area

• Stage 1: CV Screening. Your application will be reviewed within 24 hours of submission.
• Stage 2: Pre-Screening. A short Q/A session to assess your experience and technical background. You can choose between an automated Q/A video session (recommended for faster feedback) or a recruiter interview.
• Stage 3: Shortlisting. Candidates whose experience best matches the role will be shortlisted and presented to the hiring manager.

• Stage 4: Recruiter interview. A 30-minute conversation with the clients talent team.
• Stage 5: Technical interview ML exercise. 90 minutes based on a real production problem; onsite in Mountain View preferred for local candidates remote otherwise.
• Stage 6: Hiring manager interview. 30 minutes.
• Stage 7: ML system design interview. 45 minutes.
• Stage 8: CTO interview. 45 minutes mostly behavioural.
• Stage 9: CEO interview. 30 minutes culture and vision fit.

Offer and onboarding

Successful candidates will receive an offer and begin the onboarding process.

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