Platform Engineer – Self-Service Data Platform

🕒 July 30

🇩🇪 Germany – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🏗️ Platform Engineer

👻 Ghost score 13%

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Bioptimus

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2024

🏥 Healthcare

💼 Consulting

📦 Logistics

💰 $41M Series A - Bioptimus on 2025-01

Healthcare • Consulting • Logistics

Bioptimus is a company that builds foundation AI models for biology, integrating multimodal and multiscale biological data to break down data silos and accelerate biomedical research and clinical decision‑making. Its flagship models include H‑Optimus‑1 for histology/digital pathology and M‑Optimus as a universal biology model; the company partners with research institutions, clinical practices, and industry to train and deploy models securely at scale. Founded in Paris in 2024, Bioptimus focuses on applying AI to problems across biomedical research, clinical workflows, and life‑science R&D while emphasizing data quality, security, and scientific validation.

📋 Description

• Own the data & storage self-service layer. • Build and maintain the storage architecture and access tooling for large, multimodal biological datasets. • Build platform services that abstract complexity. • Create the internal services and paved roads that promote self-serve data access and processing. • Work fluently with object stores (e.g., S3) and modern storage formats. • Contribute to IaC and CI/CD, extending the team's Terraform/IaC and pipelines. • Engineer security into the data layer, implementing access control, data classification, and least-privilege access in code. • Decide what graduates into the platform versus what stays an experiment.

🎯 Requirements

• 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.

🏖️ Benefits

• Flexible/remote-friendly working • Significant room for growth at the intersection of AI and biology.