Engram Lab

ML Systems & Performance Engineer

San Francisco

Sponsorship not specifiedDetected 28 days ago

Distributed SystemsCode ReviewPlatform EngineeringMachine LearningPyTorchCybersecuritySystems EngineeringResearchCommunicationCollaboration

About the role

This role is focused on designing, optimizing, and scaling training and inference workloads -bridging the gap between cutting-edge AI research and production.
Work on distributed training - data and model parallelism, communication scheduling, and scaling efficiency across multiple GPUs and nodes.

You'll also help build the engineering team around you and influence our engineering culture as we scale.

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.
You have a bias toward action and a knack for turning research concepts into concrete, executable plans.

Designing and executing new frameworks, techniques, and systems to improve performance, reliability, latency, and efficiency.
Deep understanding of ML framework (eg. PyTorch, JAX), GPUs, distributed systems, and infrastructure.

You'll be the bridge between researchers and platform engineering, while working in deep collaboration with our customers (AI-native application-layer companies like Notion and Harvey).
There are no walls between product, research, and engineering here; delivering on our mission requires a multidisciplinary approach.
You'll set the bar for engineering at Engram: code review, testing, on-call, and a security posture that gives customers confidence in entrusting us with their most sensitive data.

This listing is sourced directly from Engram Lab's careers page and normalized into a canonical job model.