Machine Learning Engineer

Compensation: $200K–$290K + Equity

Location:

San Jose, California

An innovative AI company is expanding its systems engineering team and is searching for an engineer who enjoys making large language models faster, more scalable, and more efficient in production.

If your interests include GPU optimization, distributed computing, and extracting every ounce of performance from modern hardware, this role offers the opportunity to work on some of today's most demanding inference challenges.

Responsibilities

Your primary focus will be improving the speed and efficiency of production AI systems. You'll evaluate performance bottlenecks, build benchmarking tools, optimize inference pipelines, and help scale distributed GPU environments.

Working closely with research and infrastructure teams, you'll transform new modeling techniques into reliable production systems while continuously improving latency, throughput, and hardware utilization.

We're Looking For Someone Who Has

• A degree in Computer Science, Electrical Engineering, or a related discipline
• Strong Python and C++ (CUDA preferred) programming skills
• Knowledge of modern LLM serving technologies such as vLLM, SGLang, PyTorch, or comparable frameworks
• Understanding of GPU architecture and parallel computing
• Experience with model serving, distributed inference, quantization, or batching strategies
• Strong profiling, debugging, and systems optimization skills

Additional Experience That Stands Out

• Ray or similar distributed computing frameworks
• Performance tuning at the systems or kernel level
• High-performance computing environments

What's Offered

• Competitive salary with meaningful equity participation
• 401(k)
• Unlimited PTO
• Modern engineering workspace with premium employee amenities
• Opportunity to solve technically complex AI infrastructure challenges alongside a highly experienced engineering team