MLOps Research Engineer

Merge Labs•San Francisco, CA

13d•$200,000 - $235,000

About The Position

Merge Labs is a frontier research lab with the mission of bridging biological and artificial intelligence to maximize human ability, agency and experience. We’re pursuing this goal by developing fundamentally new approaches to brain-computer interfaces that interact with the brain at high bandwidth, integrate with advanced AI, and are ultimately safe and accessible for anyone to use. About the team Merge is building the next generation of brain-computer interfaces by combining recent advances in synthetic biology, neuroscience, AI, and non-invasive imaging. To support this mission, we are building a cross-functional data and software engineering group which supports the intersection of computational modeling, neuroscience, and biomolecular engineering. This group collaborates extensively with wet-lab scientists, automation engineers, and data scientists to build digital infrastructure that accelerates molecular discovery and device optimization. About the role We’re hiring a Research Engineer to build and own the digital infrastructure that supports Merge’s diverse computational workloads. You’ll design the distributed-training & inference, experiment-tracking, and deployment frameworks that enable data scientists to rapidly iterate on models — spanning de-novo molecular design, biophysical modeling, signal processing, and computer vision. You’ll architect systems that translate research prototypes to production grade. This is a horizontal, highly-leveraged role — success means empowering every computational scientist to move faster, with more rigor and less friction.

Requirements

Deep experience in ML infrastructure, systems engineering, and production ML workflows (training → deployment → monitoring).
Proficiency with Python, PyTorch, JAX, Kubernetes, and cloud services (AWS / GCP / Azure).
Deep experience with experiment-tracking and model-management tools (MLflow, Weights & Biases).
Strong grounding in software engineering fundamentals — version control, modular design, CI/CD, and distributed computing.
A systems-level mindset: you think in terms of model lifecycle, not just single scripts.
Experience bridging machine learning and experimental science — working with sparse, noisy, and or high-cost data.
A collaborative, systems-level mindset.

Nice To Haves

Familiarity with neuroscience

Responsibilities

Build the 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 tractable optimization objectives and encode domain specific priors and constraints.
Implement model registries, evaluation frameworks, and automated reporting for benchmarking and experiment comparison.
Define CI/CD pipelines, resource orchestration (Kubernetes, Ray, Dagster).
Define and own the ML engineering roadmap, mentoring other computational scientists and establishing best practices for code hygiene, testing, and reproducibility.