Software Engineer, ML & Scientific Services

Responsibilities

• Productize ML workflows: collaborate with researchers to translate inference, fine-tuning, and data-processing work into robust, repeatable Python services with clean APIs.
• Design and own service-layer components as the single source of truth for business logic - callable from the web app and from orchestration workflows alike, with structured inputs/outputs.
• Own secure-by-design backend practices: enforce authorization and access scoping in the service layer (not just the UI), guard against silent failures, and keep sensitive data handling correct and auditable.
• Improve robustness, observability, and usability of model-driven services.
• Contribute to API and service design (usability, versioning, and long-term stability) and to a culture of thoughtful, high-quality engineering through design discussions and code review.

Requirements

• Strong Python engineering skills and experience building/operating production backend systems.
• Experience productionizing ML or scientific computing systems - inference, training/fine-tuning pipelines, or data workflows.
• Exposure to scientific or data-intensive domains.
• Demonstrated experience building secure systems: authentication, authorization, access control, and careful handling of sensitive data.
• Experience with relational databases and well-structured service architectures (FastAPI or similar).
• Strong engineering practices: observability, documentation, maintainability.
• Familiarity with data governance and compliance frameworks (e.g. ISO/IEC 27001 or similar) and security practices like threat modeling, preferred
• Familiarity with GPU-backed environments, preferred
• Familiarity with distributed-systems fundamentals, preferred
• Strong written communication and experience working in a distributed, collaborative team.
• TECH STACK & ENVIROMENT
• Python with modern tooling (e.g. uv, pixi)
• FastAPI with a clean service-layer architecture (business logic isolated from transport)
• PostgreSQL and object storage (S3)
• Workflow orchestration (e.g. Prefect); GPU-backed compute for ML workloads
• AWS (ECS and related services)
• Infrastructure-as-code and automated CI/CD; containerized services
• Agentic coding tools (e.g. Claude Code) as part of day-to-day development
• ABOUT IAMBIC THERAPEUTICS
• MISSION & CORE VALUES

Additional Information

JOB SUMMARY Iambic is building a secure, cloud-based platform for running our ML-driven drug-discovery workflows. You'll turn machine-learning workflows into reliable, repeatable production services - giving models and data pipelines clean, well-bounded service interfaces that the rest of the platform can depend on. This role also carries explicit responsibility for secure-by-design backend engineering: because we handle sensitive data, you'll make sure authorization and access scoping are enforced in the service layer, that data governance and auditability are built in, and that we're well-positioned for compliance reviews. This role best suits an engineer who likes the boundary between ML research and production systems and treats security as a design property, not an afterthought. This position is based out of our new Ireland office.

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