Machine Learning Engineer

IC Resources; San Francisco (CA); On-site; USD 200,000 - 290,000; Full time

Benefits offered by this job

401(k)

Unlimited PTO

Modern engineering workspace

Equity participation

Job summary

IC Resources seeks an engineer to accelerate production AI systems, focusing on speed and efficiency of large language model inference. You will optimize GPU-heavy pipelines and scale distributed GPU environments in a fast-moving, innovative AI company.

You will work with research and infrastructure teams to translate cutting-edge models into reliable production systems, improving latency and throughput while leveraging modern hardware.

Qualifications

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

Responsibilities

• Improve speed and efficiency of production AI systems.
• Evaluate performance bottlenecks and build benchmarking tools.
• Optimize inference pipelines and scale distributed GPU environments.
• Collaborate with research and infrastructure teams to deploy new modeling techniques.
• Continuously improve latency, throughput, and hardware utilization.

Skills

Python

C++

CUDA

vLLM

SGLang

PyTorch

GPU architecture

Distributed inference

Profiling

Debugging

Education

Degree in Computer Science or Electrical Engineering

Tools

CUDA toolkit

Job description

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