Applied ML Engineer: Scale & Deploy Large Models

AI Breaking Wire; San Francisco (CA); Hybrid; USD 250,000 - 380,000; Full time

Benefits offered by this job

Equity

Medical, dental, and vision

Unlimited PTO

Flexible remote work

Wellness stipend

Job summary

OpenAI is seeking an Applied Machine Learning Engineer in San Francisco, CA to bridge cutting-edge research and scalable production applications. You will collaborate with researchers, product managers, and systems engineers to optimize, deploy, and scale models like GPT-4 and custom architectures.

The role requires 4+ years in software engineering with at least 2 years in ML systems, plus deep knowledge of distributed training and modern inference engines.

Qualifications

• Bachelor's or Master's degree in Computer Science or equivalent practical experience.
• 4+ years of software engineering experience, with at least 2 years in ML systems or applied ML.
• Deep understanding of distributed training, model parallelism, and modern inference engines (vLLM, TensorRT-LLM).
• Proficiency in Python, C++, and deep learning frameworks.

Responsibilities

• Scale and optimize large language models for high-throughput, low-latency inference in production.
• Build robust ML pipelines for fine-tuning, evaluation, and CI of models.
• Partner with product teams to design API endpoints and user-facing capabilities powered by advanced AI.
• Monitor model performance, latency, and cost-efficiency, driving infrastructure improvements.

Skills

Python

C++

ML systems

distributed systems

API design

Education

Bachelor's or Master's in CS or related

Tools

vLLM

TensorRT-LLM

PyTorch

TensorFlow

Job description

OpenAI is seeking an Applied Machine Learning Engineer in San Francisco, CA to bridge cutting-edge research and scalable production applications. You will collaborate with researchers, product managers, and systems engineers to optimize, deploy, and scale models like GPT-4 and custom architectures.

The role requires 4+ years in software engineering with at least 2 years in ML systems, plus deep knowledge of distributed training and modern inference engines.