Scientific Data Engineer

Merge Labs•San Francisco, CA

1d•$225,000 - $260,000

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. The Software & Data Engineering team builds the shared computational foundations that allow Merge scientists to turn complex experimental data into reliable insight. We work at the boundary between software engineering and scientific research, partnering closely with experimental teams across in vitro and in vivo programs. Our work spans reusable analysis libraries, data models, workflow infrastructure, and tools for exploring scientific results. We aim to make analyses reproducible and easy to extend while preserving the flexibility required in a rapidly evolving research environment. We are looking for a Scientific Data Engineer to build the software, data, and analytical systems that support Merge's scientific workflows. This is a software engineering role with a strong scientific computing component. Image analysis will be an important initial area of focus, alongside data modeling and analysis across other experimental modalities. You will own workflows from raw experimental inputs through validated, reproducible results. You will work directly with scientists to understand their questions, identify the reusable components behind individual requests, and turn those components into libraries and pipelines that can support multiple teams and assays. Your work will focus on the analysis of imaging and ultrasound data and on building reusable methods and systems for research spanning in vitro experiments, in vivo studies, and human work. You will help create consistent, scalable approaches to data access, analysis, validation, and exploration while adapting to the distinct scientific requirements of each modality and stage of research.

Requirements

Strong software engineering experience in Python, including API design, testing, packaging, code review, and collaborative development with Git.
Experience designing and implementing image-analysis pipelines for scientific data, and sound judgment about selecting, configuring, and validating classical or learned methods.
A rigorous approach to scientific computing, including quantitative validation, quality control, reproducibility, provenance, and explicit handling of failure modes.
Experience building production or research workflows with an orchestration system such as Dagster, Prefect, or Airflow.
Experience working directly with wet-lab scientists to clarify ambiguous needs, communicate trade-offs, and iterate toward useful and scientifically valid solutions.
A systems mindset: you can distinguish assay-specific requirements from reusable infrastructure and make thoughtful trade-offs among delivery speed, maintainability, reliability, performance, and cost.
The ownership and judgment to break ambiguous work into milestones, surface risks early, and independently drive important projects to completion.

Nice To Haves

Experience with Bazel or another large monorepo build system.
Experience with next-generation sequencing, spatial omics, ultrasound, or other scientific data modalities.
Experience working in an early-stage or rapidly changing research environment.

Responsibilities

Design, implement, and maintain reusable Python libraries for scientific and image analysis and quality control.
Build and operate reproducible analysis pipelines that integrate with workflow-management and scientific data systems.
Define data models for raw data, experimental metadata, derived results, and analysis provenance across in vitro and in vivo assays.
Partner with wet-lab and computational scientists to translate evolving scientific questions into clear requirements, validated methods, and maintainable software.
Establish appropriate testing, validation, versioning, observability, and failure-handling practices for scientific workflows.
Evaluate external methods and tools, make pragmatic build-versus-buy decisions, and define interfaces that allow systems to evolve without repeatedly rewriting downstream analyses.
Balance immediate experimental needs with investments in shared infrastructure that improve consistency, interoperability, and long-term research velocity.