MLOps Research Engineer

Merge Labs; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Merge Labs is hiring a Research Engineer to build and own the digital infrastructure that supports diverse computational workloads. You will 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.

This horizontal role empowers analysts to move faster with rigor and less friction, architecting production-grade

Qualifications

• Deep experience building ML infrastructure, scalable pipelines, and production workflows.
• Strong Python code quality, modular design, and testing practices.

Responsibilities

• Build scientific and engineering scaffolding for active-learning and closed-loop optimization, including data ETL and ML modeling.
• Collaborate with scientists to define objectives and encode priors/constraints.
• Implement model registries, evaluation frameworks, and automated reporting for benchmarking.

Skills

ML infrastructure

Python

PyTorch

JAX

Ray

Kubernetes

Cloud services

Experiment tracking

Tools

MLflow

Weights & Biases

Job description

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 developingfundamentally 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.

In this role, you will:

• 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.

You might thrive in this role if you have:

• Deep experience in ML infrastructure, systems engineering, and production ML workflows (training → deployment → monitoring).

• Proficiency with Python, PyTorch, JAX, Ray, 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.

• Familiarity with neuroscience (nice to have).

For more information about hiring at Merge, please visit our Hiring FAQ

Merge Labs does not discriminate on the basis of race, color, religion, national origin, age, sex, sexual orientation, gender, gender identity, gender expression, marital status, physical or mental disability, medical condition, genetic information, family status, ancestry, citizenship, U.S. military (state and federal) and veteran status, or any other legally protected status. It is our intention that all applicants be given equal opportunity and that selection decisions are based on job related factors. We are an equal opportunity employer.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing [e-mail].