Platform Engineer — Self-Service Data Platform

Bioptimus is building a universal foundation model for biology — learning the deep structure of living systems from data at scale, the way large language models learned the structure of text. Getting this right accelerates drug discovery, protein engineering, and genomics, and ultimately creates lasting impact for patients living with disease.

Foundation models for biology are only as good as the data platform underneath them. Massive, multimodal biological datasets have to be stored well, governed carefully, and — crucially — made genuinely self-serviceable so researchers can move without waiting on infrastructure. Building that platform is what this role is about.

This is a remote role. We’re headquartered in Paris, but the position can be performed remotely outside of Paris.

About the role

You'll join the platform organization we're building for Bioptimus's next phase of scale, owning the self-service and data surface: the storage architecture and the tooling that let engineers and researchers find, access, and process large multimodal datasets safely and on their own.

This is a hands-on, product-minded platform role. Your focus is the platform that enables others to work with data effectively — the infrastructure, paved roads, and services — rather than building bespoke pipelines as an end in themselves. You'll bring strong opinions on what should become a reusable, productized capability versus what stays a one-off, and you'll build guardrails that keep people safe without slowing them down.

You'll work cloud-first (AWS) alongside the Cloud & DevEx platform engineer, and partner with research to support their scale — supporting research workflows, not owning research-infra execution.

What you'll be doing

As a Platform Engineer, you will own the following tasks:

• Own the data & storage self-service layer. Build and maintain the storage architecture and access tooling for large, multimodal biological datasets — provisioning, access, and lifecycle, exposed as self-serve.
• Build platform services that abstract complexity. Create the internal services and paved roads that promote self-serve data access and processing, so researchers and engineers don't file tickets for routine work.
• Storage and data infrastructure. Work fluently with object stores (e.g., S3) and modern storage formats (e.g., Parquet, Delta, Iceberg); design sensible, reproducible data workflows where the platform needs them.
• Contribute to IaC and CI/CD. Extend the team's Terraform/IaC and pipelines so the data platform is reproducible and deployed like the rest of the platform.
• Engineer security into the data layer. Implement access control, data classification, and least-privilege access in code — so sensitive data is protected by default.
• Apply a product lens. Decide, with the rest of the platform team, what graduates into the platform versus what stays an experiment.

What you'll bring

The successful candidate will have a ‘team-first’ attitude; be highly organized, proactive, and detail-oriented; thrive in a fast-paced and evolving environment; and enjoy solving operational and technical challenges at scale.

• Production platform or infrastructure experience (typically 3–5+ years) with a high degree of ownership.
• Proficiency in Infrastructure-as-Code — Terraform — and hands-on with Kubernetes/Helm and containers.
• Data-platform capability — solid working knowledge of object stores, Relational databases and modern storage formats, and the data workflows teams build on top of them.
• Data/Workflow orchestration - Familiarity with data orchestration tooling (Dagster, Airflow, Prefect).
• Solid software engineering — Python (or a comparable language suitable for data and platform work), with sound engineering practices.
• Security-aware engineering — you implement access control and least-privilege in code, not as an afterthought.
• A platform-as-a-product mindset — you build self-serve capabilities that people want to use, and you enable rather than block.

How to stand out

• Experience or strong interest in running ML/AI training and inference workloads — or supporting agentic / AI-driven workflows — on the platform. This is where the data and self-service surface is heading, and curiosity here goes a long way.
• Experience with biological/medical data standards (DICOM, FHIR, omics, whole-slide images) or other large scientific datasets.
• AWS data and ML services experience.
• Exposure to high-throughput / parallel storage for GPU training (WEKA, VAST, CEPH) — a bonus given our cloud-first setup.
• Experience in pharma, biotech, healthcare, or another regulated-data environment.
• Experience contributing to or maintaining open-source projects.

Why This is a Unique Opportunity

We believe the best platforms come from ownership, strong opinions, and a genuine instinct to make other people faster. Here's what you can expect:

• Ownership — the autonomy to set direction on the surfaces you own, make the calls, and see the impact.
• Real leverage — you're a force multiplier for every engineer and researcher building on top of the foundation models.
• Foundational work — you're building the platform org from the seed, not maintaining someone else's.

In addition: a competitive salary and meaningful equity, flexible/remote-friendly working, and significant room for growth at the intersection of AI and biology.

The candidate journey

To be considered, please submit your CV in English. We believe in a transparent and collaborative interview process. Here is what you can expect after submitting your application:

1. Screening: Once you have applied, the hiring team will review your application to determine if your work experience and skills align with the necessary proficiencies of this position.
2. Hiring Manager (30 min): A discussion with the Hiring Manager to review your background, operational experience, technical fluency, and motivation for joining Bioptimus. This conversation will also explore your experience working with external partners, managing data workflows, and operating in fast-paced environments.
3. Technical Assessment: Given the technical nature of the role, you will be invited to complete a technical assessment designed to evaluate your practical skills in data operations, data handling, and workflow problem-solving. This assessment may include exercises related to data organization, scripting, SQL, cloud infrastructure, or operational reasoning.
4. Case Study: You will work through a real-world operational case study related to biomedical data onboarding, harmonization, or partner management. You will present your approach and recommendations to members of the Data, Engineering, and Partnerships teams, followed by a discussion and Q&A session.
5. Executive Interview: A comprehensive discussion with our Senior Leadership team focused on long-term vision, collaboration style, values, and mutual fit.
6. Offer: Following the completion of the interviews, our hiring team will make a final decision and will be in touch to share the outcome of your interviews. If the team would like to move forward, the recruiter will discuss the details of our proposed offer with you.
7. Onboarding: We are happy to have you joining the team. Once you have accepted and signed your offer, we will be in touch to begin the process of onboarding you to Bioptimus.