Research Engineer (ML Infrastructure)

Location: San Francisco Bay Area, United States
Work format: On-site

Salary: $200,000–$235,000 per year, plus equity.

Company

hirify.global is a frontier research lab developing high-bandwidth brain-computer interfaces that combine biology, neuroscience, artificial intelligence, and non-invasive imaging.

What you will do

• Build digital infrastructure for distributed training, inference, experiment tracking, and deployment.
• Develop scientific and engineering scaffolding for active learning and closed-loop optimization, including data ETL, ML modeling, and library design.
• Collaborate with computational scientists to define optimization objectives, domain-specific priors, and constraints.
• Implement model registries, evaluation frameworks, automated reporting, and benchmarking workflows.
• Define CI/CD pipelines and resource orchestration using Kubernetes, Ray, and Dagster.
• Own the ML engineering roadmap, mentor computational scientists, and establish practices for code quality, testing, and reproducibility.

Requirements

• Deep experience with ML infrastructure, systems engineering, and production ML workflows from training through deployment and monitoring.
• Proficiency with Python, PyTorch, JAX, Ray, Kubernetes, and cloud services such as AWS, GCP, or Azure.
• Experience with experiment tracking and model management tools, including MLflow and Weights & Biases.
• Strong software engineering fundamentals, including version control, modular design, CI/CD, and distributed computing.
• Experience connecting machine learning with experimental science and working with sparse, noisy, or high-cost data.

Nice to have

• Familiarity with neuroscience.

Culture & Benefits

• Cross-functional collaboration with wet-lab scientists, automation engineers, and data scientists.
• Work on brain-computer interfaces combining synthetic biology, neuroscience, AI, and non-invasive imaging.
• Equity offered as part of the compensation package.
• Collaborative environment focused on complementary strengths and reproducible scientific engineering.