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About the Role

You’ll be among the first ML Systems Engineers, joining a team of machine learning researchers and performance engineers in building our personalization and continual learning API. This role is focused on designing, optimizing, and scaling training and inference workloads, bridging the gap between cutting-edge AI research and production. You will set the bar for engineering at Engram, influencing our engineering culture and helping to build the team.

Responsibilities

Design and execute 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, including data and model parallelism, communication scheduling, and scaling efficiency across multiple GPUs and nodes.
Set the bar for engineering at Engram: code review, testing, on-call, and a security posture that gives customers confidence.
Help build the engineering team around you and influence our engineering culture as we scale.

Requirements

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

Skills

ML Systems Engineering
Personalization API
Continual Learning API
Training workloads
Inference workloads
Performance optimization
Reliability engineering
Latency optimization
Efficiency optimization
Distributed systems
PyTorch
JAX
GPUs
Infrastructure
Code review
Testing
On-call
Security posture

Location

San Francisco, CA

Work Type

In-person
Full-time

Experience Level

5+ years of experience

Education Level

Bachelor's degree or equivalent experience

Salary/Compensations

Competitive cash compensation
Startup equity

About the Company

Today’s AI is a brilliant stranger: it can solve the world’s hardest math problems, but it knows next to nothing about you and your work. It rereads your files to answer even basic questions, burns an enormous amount of tokens when sifting through large corpuses, and between sessions, it retains scraps at best.
We train models to study your world and anticipate your questions in advance, forming engrams: compact memories that capture your knowledge and history. Our approach opens a new axis of scaling. The more we study your context at training time, the better we become at inference time.
We're already working with leaders in AI like Microsoft, Notion, and Harvey, and just raised $98M from General Catalyst, Kleiner Perkins, Sequoia, Factory, Modern, Amplify, Neo and others. Our investors and advisors include Assaf Rappaport, Andrej Karpathy, and Pieter Abbeel.
AI has spent years learning everything about the world. Now it should learn something about yours.

Equal Opportunity

Engram is an equal opportunity employer. We’re building a team that reflects a range of backgrounds and perspectives, and we welcome applicants regardless of race, color, religion, national origin, gender, gender identity, sexual orientation, age, disability, or veteran status.