Software Engineer, ML & Scientific Services

Iambic Iambic · Pharma · Dublin, Ireland · Technology

Software Engineer to productize ML workflows into reliable, repeatable production services with secure-by-design backend engineering, focusing on authorization, access scoping, data governance, and auditability for a cloud-based ML-driven drug-discovery platform.

What you'd actually do

  1. Productize ML workflows: collaborate with researchers to translate inference, fine-tuning, and data-processing work into robust, repeatable Python services with clean APIs.
  2. 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.
  3. 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.
  4. Improve robustness, observability, and usability of model-driven services.
  5. 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.

Skills

Required

  • Strong Python engineering skills
  • experience building/operating production backend systems
  • Experience productionizing ML or scientific computing systems
  • Demonstrated experience building secure systems: authentication, authorization, access control, and careful handling of sensitive data
  • Experience with relational databases
  • well-structured service architectures (FastAPI or similar)
  • Strong engineering practices: observability, documentation, maintainability

Nice to have

  • Familiarity with data governance and compliance frameworks (e.g. ISO/IEC 27001 or similar)
  • security practices like threat modeling
  • Familiarity with GPU-backed environments
  • Familiarity with distributed-systems fundamentals

What the JD emphasized

  • secure-by-design backend engineering
  • sensitive data
  • authorization and access scoping
  • data governance and auditability
  • secure systems

Other signals

  • Productize ML workflows
  • secure-by-design backend engineering
  • translate inference, fine-tuning, and data-processing work into robust, repeatable Python services