Engineering Manager - Machine Learning
Recursion
8 days ago
Remote friendly (Salt Lake City, UT)
United States
IT
In This Role You Will
- Enable AI/ML, LLM, and Agentic Systems teams for scale by building and operating platforms that allow data scientists/ML engineers to train, deploy, and monitor models across massive datasets; support model development, training, and deployment.
- Act as a mentor, coach, and sponsor; share technical, leadership, and managerial skills in MLOps, distributed computing, and infrastructure engineering; partner across ML research, platform engineering, and business teams.
- Enable a model-driven culture by ensuring ML infrastructure supports rapid experimentation, reliable model deployment, and continuous improvement (e.g., GPU cluster utilization optimization, agentic orchestration, and company-wide MLOps standards).
The Experience You Will Need
- Hands-on tech lead or manager experience focused on infrastructure, MLOps, and distributed systems; enthusiasm for deep technical work around ML, orchestration, and agentic systems.
- People-first mindset; understanding of Conway’s Law impact on ML system outcomes.
- Track record of learning from and teaching peers in ML infrastructure, model deployment, distributed compute, GPU optimization, and MLOps system architecture.
- Experience with Python, PyTorch, Docker, Kubernetes, Ray, Weights & Biases, Prefect, BigQuery, Postgres, GCP, CUDA, and model serving frameworks.
- Life sciences/drug discovery fluency is a plus (not required).
Working Location & Compensation
- Hybrid, office-based role at US headquarters in Salt Lake City, Utah (in-office at least 50%); estimated annual base range: $151,130 to $203,490 (USD); eligible for annual bonus, equity, and comprehensive benefits package.
- Enable AI/ML, LLM, and Agentic Systems teams for scale by building and operating platforms that allow data scientists/ML engineers to train, deploy, and monitor models across massive datasets; support model development, training, and deployment.
- Act as a mentor, coach, and sponsor; share technical, leadership, and managerial skills in MLOps, distributed computing, and infrastructure engineering; partner across ML research, platform engineering, and business teams.
- Enable a model-driven culture by ensuring ML infrastructure supports rapid experimentation, reliable model deployment, and continuous improvement (e.g., GPU cluster utilization optimization, agentic orchestration, and company-wide MLOps standards).
The Experience You Will Need
- Hands-on tech lead or manager experience focused on infrastructure, MLOps, and distributed systems; enthusiasm for deep technical work around ML, orchestration, and agentic systems.
- People-first mindset; understanding of Conway’s Law impact on ML system outcomes.
- Track record of learning from and teaching peers in ML infrastructure, model deployment, distributed compute, GPU optimization, and MLOps system architecture.
- Experience with Python, PyTorch, Docker, Kubernetes, Ray, Weights & Biases, Prefect, BigQuery, Postgres, GCP, CUDA, and model serving frameworks.
- Life sciences/drug discovery fluency is a plus (not required).
Working Location & Compensation
- Hybrid, office-based role at US headquarters in Salt Lake City, Utah (in-office at least 50%); estimated annual base range: $151,130 to $203,490 (USD); eligible for annual bonus, equity, and comprehensive benefits package.