Principal Software Engineer (AI Systems & Data Pipelines)

Join to apply for the Principal Software Engineer (AI Systems & Data Pipelines) role at IONLACE

Principal Software Engineer (AI Systems & Data Pipelines)

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Join to apply for the Principal Software Engineer (AI Systems & Data Pipelines) role at IONLACE

We are rebels

IONLACE is a place for rebels. We are on a mission to rewrite the timelines of breakthroughs in medicine.

We are rebels

IONLACE is a place for rebels. We are on a mission to rewrite the timelines of breakthroughs in medicine.

You will be joining a team with experience from leading biotechnology companies and institutions across the world. You will be working at the cutting edge of machine learning and synthetic biology. We operate as a fast-paced startup with our office and lab at the Karolinska Institute in Stockholm. Join us and build the future.

About The Role

We are looking for a Principal Engineer / Engineering Lead to build the core systems powering our AI-driven agentic protein engineering platform. This is a hands-on senior engineering role where you will design and deliver systems while guiding others in technical best practices. You’ll work across the full stack, building production systems that integrate complex biological data, ML model outputs, and user-friendly scientific interfaces.

A critical part of the role is developing systems that integrate feedback from the Design–Build–Test–Learn (DBTL) cycle, incorporating in-house wet lab experimental data into dry lab computational pipelines, enabling rapid iteration and continuous improvement in our protein engineering workflows.

You will need both the ability to ship code at speed and plan system architecture from day one. Over time, this position can remain a senior individual contributor role or expand into more formal management, depending on your strengths and ambitions.

Responsibilities

Build, Design, and Scale

• Write and deliver production-grade features and infrastructure in Python (FastAPI, PostgreSQL) and JavaScript/React, Tailwind, Next.js. Review and debug code written by the team.
• Drives decisions that influence the product design, application functionality, and technical operations and processes.
• Plan and implement architectures for high-performance protein engineering pipelines.
• Translate ambitious product and research requirements into clear technical milestones.
• Operationalize AI workflows from research prototypes into reproducible, production-ready systems used daily by scientists.
  
  

• Build intelligent interfaces that integrate agentic tools into scientists’ workflows.
• Develop seamless full-stack integrations between AI agents, user inputs, and large-scale biological datasets.
  
  

• Deploy and maintain containerized workflows (Docker, Kubernetes) in cloud-native environments (GCP preferred).
• Create high-throughput data integration pipelines connecting experimental, computational, and AI-driven outputs.
  
  

• Design and implement systems to capture and process wet lab experimental data in near real-time.
• Incorporate this feedback into computational models and protein design workflows, enabling rapid iteration through the DBTL cycle.
• Collaborate closely with wet lab scientists to ensure data quality, interoperability, and traceability across the platform.
  
  

• Conduct code reviews, share best practices, and help engineers solve complex technical challenges.
• Contribute to project planning to ensure timelines are realistic and delivery is on track.
  
  

• Proven track record of shipping production systems in Python (FastAPI, PostgreSQL) and JavaScript/TypeScript (React, Next.js).
• Strong background in distributed systems, scalable architectures, and data-intensive applications.
  
  

• Experience deploying ML models into production, ideally with multi-agent orchestration (LangGraph, Pydantic, or custom frameworks).
• Familiarity with MLOps best practices, model lifecycle management, and CI/CD workflows.
  
  

• Experience designing data systems that bridge laboratory instrumentation and computational workflows.
• Familiarity with handling experimental datasets, scientific data formats, and lab data management systems is a plus.
  
  

• Bonus: experience with computational biology, protein engineering, or biotech data workflows.
  
  

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

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