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 Senior / Principal ML 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).
If you're excited about this role but don't meet every qualification, please apply. As we build, we're hiring for complementary strengths to form a high-impact team.
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 [email protected].
Skills Required
- Deep experience in ML infrastructure, systems engineering, and production ML workflows (training -> deployment -> monitoring).
- Proficiency with Python.
- Proficiency with PyTorch and JAX.
- Experience with Ray for distributed computing.
- Experience with Kubernetes for orchestration.
- Experience with cloud services (AWS, GCP, or Azure).
- Experience with experiment-tracking and model-management tools (MLflow, Weights & Biases).
- Experience designing distributed training and inference pipelines, model registries, and evaluation frameworks.
- Experience defining CI/CD pipelines and resource orchestration (Dagster, Kubernetes, Ray).
- Strong software engineering fundamentals including version control, modular design, testing, and reproducibility.
- Experience working with sparse, noisy, or high-cost experimental data and bridging ML with experimental science.
- Familiarity with neuroscience.
What We Do
Merge Labs is a research lab with the long-term mission of bridging biological and artificial intelligence to maximize human ability, agency and experience. Join us: merge.io/careers
