[Hiring] Software Engineer - Research Data Platform @Bioptimus

Role Description

We are a fast-moving, data-centric start-up on a mission to bridge the gap between complex biological data and cutting-edge AI. As a Software Engineer in our research data platform team, you will help develop the backbone of our data architecture, designing and scaling the systems that power our AI models and user-facing tools, both internal and external.

We are looking for someone passionate about scalable, efficient, and highly structured data storage. In particular, we are looking for someone interested in designing systems that account for the complex structures inherent in biological data. You will build clean, maintainable systems that make massive biological datasets accessible, reliable, and actionable. If you love optimizing performance, improving schemas, and seeing your work directly empower a broad audience of stakeholders—from scientists and product engineers to AI agents—you will fit right in.

This is a mid-level to senior individual contributor role. You will collaborate closely with our multidisciplinary team of researchers and engineers to drive software development and productization efforts.

What You Will Be Doing

• Architect and build for performance:
  • Design, implement, and maintain robust, scalable data schemas and storage solutions optimized for high-performance AI workloads.
• Optimize storage formats:
  • Benchmark, profile, improve, and extend distributed storage using chunking, compression, parallelization, and custom solutions.
• Produce clean code:
  • Develop and maintain high-quality, production-ready data systems, following clean code principles and engineering best practices.
• Design interfaces for people and AI agents:
  • Build clean, typed, well-documented APIs that let researchers, engineers, and AI agents query and extend the platform programmatically.
• Collaborate and drive delivery:
  • Work with researchers and product engineers to scope needs, align on priorities, and own projects end-to-end.
• Performance tuning:
  • Monitor, profile, and optimize database queries, storage read and write paths, pipeline bottlenecks, and cloud infrastructure costs.
• Data governance and security:
  • Collaborate with platform engineers to implement rigorous data validation, testing, versioning, and access control.

Qualifications

• Technical and Professional Qualifications
• Python expertise:
  • Deep, production-level knowledge of Python with a passion for clean, readable, and highly maintainable code.
• Backend frameworks:
  • Strong hands-on experience with modern Python data tools and frameworks, such as Pydantic (data validation), SQLAlchemy (ORM), Alembic (database migrations), object storage abstractions, and FastAPI or similar frameworks.
• Structured databases:
  • Expertise in relational database management systems (RDBMSs) such as PostgreSQL, including schema design, indexing strategies, and query optimization.
• Interfaces and agents:
  • Experience designing API surfaces and exposure to protocols for programmatic and agent access such as the Model Context Protocol (MCP).
• User-centric mindset:
  • A strong belief that data infrastructure is a product, combined with a commitment to keeping it usable and accessible to non-technical stakeholders.

Requirements

• How to Stand Out
• Each of the following would be a valuable bonus, not a requirement:
  • Biotech/life sciences affinity:
    • Prior experience handling biological data formats (e.g., histology, transcriptomics, genomics, proteomics, or clinical trial data) or working in a biotech/health-tech environment.
  • Start-up agility:
    • A proven track record of thriving in fast-paced, ambiguous startup environments in roles requiring high autonomy and ownership.
  • Array and storage formats:
    • Experience with efficient distributed array storage (e.g., xarray, Zarr, TileDB, and TIFF) for both dense and sparse data, and comfort working close to library internals.
  • Workflow orchestration:
    • Experience with orchestration tools such as Dagster, Airflow, Prefect, or database-backed work queues.
  • Frontend and visualization:
    • Experience building or integrating with frontend and visualization tools that make data explorable.
  • Proactive communicator:
    • Ability to translate complex data architecture concepts into clear explanations for scientists, product managers, and engineers alike.

Benefits

• A collaborative and mission-driven work environment at the forefront of biology and AI.
• The opportunity to help grow our data platform and accelerate discovery in cancer biology.
• Competitive salary and equity package.
• Flexible work arrangements, including remote options.

Candidate Journey

• To be considered, please submit your CV in English.
• We believe in a transparent and collaborative interview process. Our goal is to determine whether there is a strong mutual fit. Here is what you can expect after submitting your application:
  • Screening:
    • An initial 30-minute introductory call with our in-house recruiter.
    • A 30-minute with the hiring manager to discuss your background, motivations, and the position in more detail.
  • Interviews:
    • Following a successful screening, you will be invited to a series of interviews:
      • System design (60 min):
        • A discussion of schema design, evolution, and migration; storage formats and performance trade-offs; and API design.
      • Take-home project:
        • Based on the outcome of the previous call, you will receive a sample system and an accompanying assignment. This assignment covers topics similar to those discussed in the previous system-design interview. You will submit a brief report and Python code.
      • Assignment presentation and technical Q&A (60 min):
        • You will discuss your take-home assignment with a panel of two or three interviewers. During the first 20 minutes, you will present and demonstrate your solution, followed by a Q&A. You may also briefly introduce your relevant past experience.
      • Executive Interview (30 min):
        • A discussion with one or more members of our senior leadership team focusing on the company’s vision, cultural fit, shared expectations, and growth potential.
  • Offer:
    • Following the completion of all interviews, our hiring team will make a final decision. Please note that an offer is contingent upon the successful completion of a reference check.
  • Onboarding:
    • We will be happy to welcome you to the team. Once you accept and sign your offer, we will begin the onboarding process.