Software Engineer; Agentic

Software Engineer (Agentic) – Biographica

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🌽 Safeguarding the future of food. Biographica is on a mission to accelerate the development of more productive, sustainable, nutritious & climate‑resilient food sources. We’re building the world’s first ML‑driven target discovery platform for crop gene‑editing.

🧬 Target discovery for gene‑editing. Identifying which genes to edit and how remains a significant challenge. We use cutting‑edge deep learning to accurately and efficiently identify high‑value genetic targets for crop gene‑editing, drawing inspiration from the drug‑discovery space and building a best‑in‑class discovery platform for plant sciences.

🤖 Building agent‑native scientist‑in‑the‑loop discovery. Our next chapter is to productise target discovery so we can deliver deep, traceable discovery projects quickly and in parallel. We believe scientific agents, grounded in Biographica‑curated data and proprietary models, will be key to scaling both the breadth and depth of our analyses.

👥 Team. Led by co‑founders Dom (CTO) and Cecy (CEO), we are a team of 13, including 2 ML engineers, 2 data engineers, 3 bioinformaticians & 2 experimental scientists. We primarily work in person from our office in Spitalfields, London, 4 days per week. This role will be based in London, with close collaboration across ML, data and scientific product.

• Designing and implementing the core orchestration layer for scientist‑in‑the‑loop, agent‑based discovery workflows that plug into our existing stack (Dagster assets, MLFlow model registry, internally curated plant databases).
• Building a robust tool registry and execution framework so agents or scientists can safely call Biographica tools (model inference, dataset retrieval/QC, literature search, bioinformatics workflows, etc.) with clear inputs/outputs.
• Establishing provenance, logging, and evaluation infrastructure so every agent run is reproducible and reviewable by scientists and customers.

• Own the architecture and implementation of our agentic workflows (Python‑first) including state management, retries, branching, and human checkpoints.
• Build and maintain a tooling interface layer that standardises how agents call internal services and external APIs, with strong typing, validation, and error handling.
• Implement provenance and traceability: versioned inputs, prompt/tool versions, dataset/model pointers, and run‑level audit trails.
• Create an evaluation harness for agent or scientist performance (e.g., correctness of exclusions, precedent accuracy, evidence coverage), and integrate lessons from customer projects into continuous improvement.
• Partner closely with ML and bioinformatics teams to expose models and data as agent‑safe tools, and with scientific product to ensure workflows match real discovery practice.
• Contribute directly to near‑term customer deliverables by shipping a minimum viable discovery workflow aligned to long‑term product architecture.

• Help shape engineering standards for agent reliability, safety, and interpretability in a scientific context.
• Support internal documentation, developer experience, and onboarding as the agent platform becomes a shared foundation across the company.
• Potentially mentor future hires in agent engineering, orchestration, and platform work.

• Strong software engineering background (industry or research), with deep experience building production Python systems.
• Track record designing and deploying workflow/orchestration systems (e.g., DAGs, event‑driven services) in complex domains.
• Experience working in scientific, biotech, or high‑integrity domains where reproducibility and auditability matter.
• Hands‑on experience working close to model APIs while keeping a clean abstraction boundary.
• Excellent systems thinking: ability to balance speed with architecture, and to design modular interfaces that prevent tooling sprawl.
• Experience implementing logging, observability, and evaluation for ML/AI systems.
• Ability to communicate clearly across disciplines. You will work daily with…