Substrate Bio
AI Engineering Lead
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Senior AI Engineer – Intelligence Software Product Lead
The Opportunity
Substrate is building a network of fully autonomous wet labs, cloud-based data production facilities for AI biology, integrated with foundation models to become the critical infrastructure layer for AI-driven biological discovery. Our first node opens in King’s Cross, London, with multiple integrated workcells and two scientific verticals online by mid-2027. Our customers range from foundation model labs to global pharma.
We are hiring an AI engineering lead—our first engineering hire on the intelligence software product. Substrate already runs two AI products atop operational data, with more to come. You’ll write large swathes of the first production code, shape architecture, and influence how the subsequent team is built.
About Substrate
Substrate is a spin-out from Automata, the UK lab automation company behind the workcell platform powering our autonomous labs. Our founding team includes:
- Mostafa ElSayed (CEO and Automata founder)
- Oli Hoy (ex-VP Customer Experience, Automata)
- Alexey Morgunov (AI Scientist co-founder, leading intelligence software)
- A founding biology lead to join shortly.
We aim to reach 32 people by Q1 2027, funded by venture capital + government grants. Unlike traditional cloud labs or CROs, we’re an autonomous lab platform with closed-loop integration—offering operational transparency that unlocks unprecedented AI-driven biological discovery.
The Role
You’ll sit alongside Alexey Morgunov as the AI Engineering Lead for the intelligence software product, where most of the early production code will be yours to write. The product currently has two key surfaces:
- AI Scientist: A data-driven agent that ingests the scientific literature, identifies inconsistencies and critical knowledge gaps, and flags high-value experiments—directing Substrate’s reserved R&D capacity toward resolving them.
- AI Assays: A continuous improvement system that leverages run metadata to optimize assay protocols iteratively, reducing variable costs and increasing throughput.
You’ll collaborate with:
- Alexey (technical direction)
- The founding software engineer (bridging operational and intelligence software)
- The founding biology team and vertical leads (defining scientific input)
Your First 12 Months
Phase 1: Sep – Dec 2026
- Land in the team and shape the architecture for AI Scientist alongside its fundamental infrastructure.
- Ship the first "thin slice" end-to-end: literature ingestion, gap identification, and a functional agentic loop suggesting candidate experiments.
- Establish the engineering culture: code reviews, deployment workflows, observability frameworks, and tools you’ll maintain.
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Phase 2: Jan – Mar 2027
- Build data capture infrastructure for AI Assays, defining data schemas and feedback loops into the system.
- Deploy AI Scientist in production, wiring its outputs into Substrate’s reserved R&D allocation across scientific verticals.
- Co-author the team roadmap as the first AI Engineer (with incoming Data Engineer and Junior AI Engineer).
Phase 3: Mar – Jun 2027
- Ship the first version of AI Assays, integrating protocol optimization into the assay design pipeline.
- Lead scoping of the third intelligence product, leveraging the lab’s now scaled operational data.
- Shift from primary coding to coordinating the intelligence team’s multidisciplinary work.
Who You Are
We’re seeking a highly experienced AI engineer with production-grade, battle-tested LLM infrastructure experience—not prototyping or demos.
Non-Negotiable
✅ 5+ years of professional software engineering, including:
- Direct experience ** deploying LLMs/foundation models/agentic systems at scale** (e.g., calling them reliably with cost constraints).
- Expertise in the LLM harness layer: orchestration (token economics, retrieval), evaluation pipelines, structured outputs, and large-scale data flow.
- Hands-on strength in Python.
- Track record of designing systems that subsequent teams reliably built on top of.
✅ Engagement with Biology/Science:
- Experience in/near biotech, scientific computing, wet-lab research, or open knowledge datasets—e.g., understanding experimental metadata, parsing PubMed data, or clinical trial infrastructures.
- If you don’t have formal background but have deeply engaged with the field (e.g., consumed papers critically), strait Angle the experience.
Bonus if you’ve:
- Worked with LIMS (Lab Information Management Systems), ELNs (Electronic Lab Notebooks), or scientific data pipelines.
- Stepped into an early-stage founding engineer role in a venture-backed company.
Why This is Radical
This is not a traditional AI engineering role. Most companies build wrappers around LLMs or user-facing agents—we’re creating AI products that drive wet labs (literally).
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- AI Scientist makes decisions—not just recommendations—on which experiments Substrate should run, based on literature gaps.
- AI Assays iteratively improves the lab’s own processes using its own data, akin to how AI powers the best-in-class bioLabs today—but with the twist: you control the research itself.
- Some find this exhilarating; others, unsettlingly open-ended. Not every engineer has the appetite.
Compensation & Equity
- Salary is competitive with senior AI/engineering roles in London, adjusted for seniority and scope. We’ll discuss specifics postlate-stage.
- Equity is meaningful with:
- Standard 4-year vesting (1-year cliff).
- Sessions to discuss the philosophy and maths.
How We Work
- Flexible in theory, RoI-based in practice: We’ll accommodate strong candidates but expect some in-person time in King’s Cross during critical phase.
- Operations work around team cadences:
- Weekly rhythms: Monday planning, Friday close.
- Quarterly all-hands offsite + 30 days leave + learning budget.
- Culture: Direct but disciplined—documentation is sacred, and team members challenge one another.
The Team
You’ll report to Alexey Morgunov—our co-founder and AI Scientist focus:
- Work closely with the foundation software engineer ( opera→AI switching layer).
- Partner with vertical team leads (proteomics, functional genomics) to equip AI Scientist with scientific context.
This role is AI Engineer #1 in what becomes a scrap of 3–4 people, active in a scalable team of 12–15 by June 2027, within an organization of 32 by Q1 2026.
How to Apply
No PM fiction. No heavily curated memos. Submit anything you think works as proof:
- GitHubs showcasing systems you’ve built.
- Essays evaluating a model.
- Cungeurous thoughts on biological data tasks.
- Idea catalysts where usefulness scaled beyond prototype.
Process:
- Initial insight chat with Alexey.
- Technical riff:
- Architecture session: How would you design AI Scientist’s foundation?
- Team growth session: How would you grow an engineering practice from scratch?
- Team demo: The final in-person setter covering scope, offers, and unfiltered probatofollows.
- Move fast if we’re aligned; expect 2–3 weeks ideal-case end-to-applicability.
Encourage ambiguity. The most useful conversations so far wg people had questions.
We’re equal-opportunity orgs—hiring on merit, scope alignment, chemistry. We accept submissions from all backgrounds.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
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