Remote ML Infrastructure Engineering Lead

Recursion; United States; On-site; USD 149,345 - 201,087; Full time

Job summary

Recursion is seeking an experienced leader to guide the ML infrastructure team, building scalable platforms for training, deploying, and monitoring models across vast datasets. You will mentor engineers, collaborate with ML researchers, and drive orchestration and MLOps standards across the organization.

The role emphasizes ownership, cross-team collaboration, and delivering fast, reliable infrastructure improvements that enable rapid experimentation and impactful drug discovery outcomes.

Qualifications

• Hands-on technical role as a tech lead or manager with a focus on infrastructure, MLOps and distributed systems.
• People-first mindset prioritizes teammates' growth and experience and acknowledges Conway's Law shaping ML outcomes.
• Proven ability to learn and teach peers in ML infrastructure, model deployment, distributed compute, GPU optimization and MLOps system design.
• Willingness to learn parts of the ML stack you may not know; current stack includes Python, PyTorch, Docker, Kubernetes, Ray, Weights & Biases, Prefect, BigQuery, Postgres, GCP, CUDA and various model serving frameworks.
• Fluency in life sciences or drug discovery is a plus but not required.

Responsibilities

• Lead and mentor a team to build, scale, and optimize ML infrastructure for Recursion's drug discovery platform.
• Collaborate across ML engineering, data science, and research to translate requirements into robust infrastructure solutions.
• Ensure rapid experimentation, reliable deployment, and scalable model serving across on-prem and cloud environments.

Skills

MLOps

Distributed systems

GPU optimization

Model deployment

Python

PyTorch

Docker

Kubernetes

Ray

Weights & Biases

Prefect

BigQuery

Postgres

GCP

CUDA

Model serving

Tools

Python

PyTorch

Docker

Kubernetes

Ray

Weights & Biases

Prefect

BigQuery

Postgres

GCP

CUDA

Job description

Recursion is seeking an experienced leader to guide the ML infrastructure team, building scalable platforms for training, deploying, and monitoring models across vast datasets. You will mentor engineers, collaborate with ML researchers, and drive orchestration and MLOps standards across the organization.

The role emphasizes ownership, cross-team collaboration, and delivering fast, reliable infrastructure improvements that enable rapid experimentation and impactful drug discovery outcomes.