[Hiring] Senior Machine Learning Engineer @AIDA Recruitment

Role Description

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.

The team owns how our client’s 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.

Responsibilities

• 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.

Qualifications

• 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.

Nice to have

• 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.

Benefits

• Fast-growing AI company (realtime voice and LLM);
• Onsite in Mountain View, CA (preferred), or remote in the US, Canada, UK, Germany, Switzerland or Serbia;
• Full-time engagement;
• Permanent employment contract;
• Salary: $250K–$300K base + competitive equity;
• H-1B / OPT transfers supported; relocation assistance to the San Francisco Bay Area.

Selection Process

• 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 client’s 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.