Founding Engineer

Founding Engineer | AI Infrastructure Startup for Biopharma | Full-Time | Up to £150K + Founding Equity

About the Company

Our client is an AI infrastructure company transforming how commercial decisions are made across the global pharmaceutical and biotechnology industries. The team unites cutting-edge AI research with decades of pharmaceutical industry leadership in portfolio strategy, competitive intelligence, forecasting, and business development.

The company builds custom, proprietary AI infrastructure that replaces legacy research vendors and noisy manual workflows with real-time, automated intelligence. Their platform continuously monitors global data sources - including clinical and regulatory milestones, deal flow, hiring, epidemiology, scientific congresses, and HCP research - slashing deliverable turnaround times by over 90% and empowering top-10 global biopharmas to act on verifiable, traceable insights.

Backed by top-tier venture capital, they build forward-deployed systems engineered directly into client environments, pairing high-impact AI capabilities with enterprise-grade data security and governance.

About the Role

Reporting directly to the founders, the Founding Engineer sits at the critical intersection of core systems architecture and client-facing technical execution. This is not an isolated back-office developer seat; it is a high-visibility, high-horsepower role designed for someone who can write production code in the morning and lead technical problem-scoping sessions with enterprise pharma stakeholders in the afternoon.

You will help design the core multi-source data and LLM extraction infrastructure, while embedding directly with client teams to discover requirements, scope custom AI modules, and ship live, secure solutions in weeks.

Key Responsibilities

• Partner directly with senior biopharma strategy and competitive intelligence leaders to unpack complex commercial problems, translate ambiguous needs into technical specs, and deploy custom modules rapidly into client environments.

• Build and maintain scalable, reliable data pipelines ingesting multi-modal global life-sciences data (clinical registries, conference abstracts, regulatory filings, and market intelligence).

• Develop robust inference, vector search, and extraction workflows using modern foundation models, with uncompromising standards for zero-hallucination provenance and source-document traceability.

• Implement robust, SOC 2–aligned cloud environments featuring fine-grained role-based access control (RBAC), end-to-end encryption, and isolated client data perimeters.

• Move seamlessly from whiteboard discovery to working client software within weeks, maintaining tight feedback loops to iterate on features in real time.

Skills & Competencies

• High EQ, poise, and communication clarity to sit across from enterprise executives, ask the right questions, and defend technical trade-offs without buzzwords.

• Strong hands-on engineering capabilities across modern backend stacks (Python/TypeScript), cloud infrastructure, distributed data ingestion, and API integration.

• Hands-on experience implementing production RAG, document parsing, and structured extraction pipelines where accuracy and auditability are non-negotiable.

• A bias toward shipping; comfortable building 80/20 MVPs to validate requirements quickly before hardening them into scalable infrastructure.

• Proven ability to break down unfamiliar domains (like complex oncology clinical trial data or pharma licensing terms) and map them cleanly into data structures and business logic.

Qualifications & Experience

• 3+ years of professional software engineering experience, ideally with exposure to client-facing engineering, forward-deployed engineering (FDE), or high-velocity B2B SaaS environments.

• Strong academic credentials or concrete evidence of top 1% technical ability (e.g., First Class / Distinction degrees in Computer Science, Mathematics, Physics, or Engineering from leading institutions; competitive programming achievements; or standout contributions to technical systems).

• Experience engaging directly with end-users or enterprise clients to gather requirements, run workshops, and iterate on live feedback.

• Solid software fundamentals: deep understanding of distributed systems, databases, cloud architecture, and modern AI/LLM tooling.