Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

Tailor your resume to this Research Engineer, ML Infrastructure role.

Rezi rewrites your resume against Cognition's job description. Free.

Resume score gauge reading 58 out of 100

Don't guess if your resume is good enough.

See how it scores against the Research Engineer, ML Infrastructure posting at Cognition — free, in seconds.

About the Role

This role focuses on owning and developing the core systems that power AI research, including distributed training infrastructure, experiment orchestration, data pipelines, and tooling. The goal is to ensure the research frontier is not slowed by infrastructure limitations. You will work closely with researchers to understand their needs and build scalable, reliable systems capable of handling large-scale training jobs across thousands of GPUs.

Responsibilities

Build and own systems for reliable, large-scale distributed training jobs across GPU clusters.
Own infrastructure for running hundreds of thousands of concurrent coding agent rollouts in VM sandboxes.
Profile and improve end-to-end training throughput, identifying and resolving bottlenecks.
Design and maintain systems for launching, tracking, and analyzing research experiments.
Build high-throughput, reliable data pipelines for training and evaluation.
Diagnose and resolve training failures across GPUs, networking, numerics, and data.
Implement and optimize parallelism strategies for large model training.
Anticipate future research needs and build enabling infrastructure proactively.

Requirements

Deep experience building and operating distributed training systems for large models.
Comfortable owning infrastructure end-to-end, from cluster level to training loop.
Strong systems engineering fundamentals, including distributed systems, networking, and storage.
Ability to reason about performance across the full hardware-software stack.
Proficiency in Python and C++.
Experience with PyTorch or equivalent deep learning frameworks at a systems level.
Hands-on experience with GPU performance profiling, memory optimization, and compute efficiency.
Experience implementing or optimizing parallelism strategies for large model training.
Track record of building tooling and abstractions that accelerate research workflows.
Strong debugging instincts across complex, distributed systems.
Sufficient ML knowledge to engage substantively with researchers.

Skills

Python
C++
PyTorch
Distributed Systems
Networking
Storage
GPU Performance Profiling
Memory Optimization
Compute Efficiency
Parallelism Strategies (Data, Tensor, Pipeline, Sequence)

Location

Remote

Work Type

Full-time

Experience Level

Senior

Education Level

PhD (preferred, but capability is prioritized)

About the Company

An applied AI lab building end-to-end software agents.
Makers of Devin, the first AI software engineer.
Team comprises world-class competitive programmers, former founders, and leaders from top AI companies.
Focuses on solving major world problems and building AI for real-world tasks.
Small, highly selective team where research and product move together.
Prototypes reach real deployment quickly.
Rewards speed, autonomy, and technical depth with minimal process overhead.
Operates in a competitive and fast-moving AI landscape.

Equal Opportunity

Cognition is an equal opportunity employer.
Does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic under applicable law.
Committed to providing reasonable accommodations for candidates with disabilities throughout the hiring process.