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# ML Systems & Performance Engineer

- **Company:** Engram Lab Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Systems Engineering, Distributed Computing Environment, Distributed Systems, Machine Learning, Microsoft Software, Open Source Technology, Graphics Processing Unit (GPU), Pytorch, Information Technology, Low Latency, Machine Learning Operations
- **Published:** November 4, 2026
- **Apply:** https://www.careerbuilder.com/job-details/ml-systems-performance-engineer-san-francisco-ca--26f27fd9-0070-4868-bdb5-a0c0a87d5780

## About the Role

Performance Engineering, Systems Engineering, Mathematics, Artificial Intelligence (AI), Microsoft Product Family, Power Amplifier, Investment Services, Performance Engineering, Systems Engineering, Application Programming Interface (API), * Bachelor's degree or equivalent experience in computer science, engineering, or similar.

 * 5+ years of experience with training or inference systems, optimized workloads with measurable results.
 * Strong engineering foundation, with demonstrated excellence navigating complex technical environments and shipping high-quality code in a fast-paced environment.
 * Deep understanding of ML framework (eg. PyTorch, JAX), GPUs, distributed systems, and infrastructure.
 * Operate well in ambiguous environments - you will have real ownership and be responsible for steering the ship in a novel sector of the industry.
 * You have a bias toward action and a knack for turning research concepts into concrete, executable plans.

Bonus points if you have

 * Prior early-stage experience.
 * Experience in open-source ML or systems infrastructure projects.

## Description

You'll be among the first ML Systems Engineer, joining a team of machine learning researchers and performance engineers in building our personalization and continual learning API, which powers models and agents that learn from user context.

This role is focused on designing, optimizing, and scaling training and inference workloads -bridging the gap between cutting-edge AI research and production. This includes:

 * Designing and executing new frameworks, techniques, and systems to improve performance, reliability, latency, and efficiency.
 * Partner closely with researchers, turning prototypes into systems that run at scale and feeding systems constraints back into research decisions.
 * Optimize serving paths for personalization and memory retrieval, where per-user state and low latency both matter.
 * Work on distributed training - data and model parallelism, communication scheduling, and scaling efficiency across multiple GPUs and nodes.