[Hiring] Senior Machine Learning Engineer @American IT Systems

Role Description

Evergreen roles with continuous hiring for staff/principal MLE and research scientist positions. High competition with top AI labs and big tech companies; expanding efforts to Europe.

Qualifications

• Research Scientist: Must have experience in foundation model training and voice/speech AI.
• MLE: Focus on inference, optimization, ultra-low latencies; skills in vllm, trt, LLM internals, CUDA C.
• Preferred companies: Google, DeepMind, Meta, TikTok, OpenAI.
• PhD required for Research Scientist; publication history is important.

Requirements

• 5+ years of experience as an ML inference or systems engineer.
• Experience building backend or ML systems and serving machine learning models in production.
• Proficiency in systems programming and GPU optimization (C++, CUDA, Rust, or optimized Python).
• Experience in model acceleration techniques (quantization, distillation, speculative decoding, caching strategies, continuous batching, or paged attention).
• Experience in distributed systems and scaling (Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, or high concurrency).

Benefits

• Located in the United States, Canada, United Kingdom, Germany, Switzerland, or Serbia.
• Ability to commute to the office in Mountain View, CA.

Ideal Candidate Profile

• Staff/principal level, ideally 6+ years of experience; open to exceptional candidates with less experience.
• Not too senior; preference for candidates comfortable in fast-paced, agile environments.
• Avoid candidates who have been with a single company for over 10 years; prefer agile and adaptable individuals.
• Red flag for candidates who frequently change jobs, especially under a year at multiple places.

Dealbreaker

• Experience at Senior level (Google L5 or Meta E5 equivalent; Inference Engineer, Senior Software Engineer).
• Experience in machine learning inference optimization (vLLM or TRT-LLM).

Miscellaneous

• More than two tenures of one year or less, currently unemployed or laid off, or has been impacted by layoffs more than twice.
• Tenure of 10 or more years at a single company.
• Typical failure modes: recsys, CPU services, agents, training or fine-tuning, edge or AV TensorRT work.
• Contractors or consultants.

About this role

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

• 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.