Principal Engineer

About the role

Pioneering Intelligence is looking for a Principal Software Engineer to join a small, senior team building the digital and AI platform that powers drug discovery and scientific research across the Flagship Pioneering ecosystem. This is a senior individual contributor role that will report directly to the Vice President of Engineering and operate as the technical authority for one or more product areas - designing systems end-to-end, building hands-on, and engaging directly with the scientists, ML researchers, and strategists who use what you build. This is not a role for engineers who want to be shielded from ambiguity or who need a product manager to translate user needs into tasks. You will own the full technical stack for your product area: from understanding what scientists actually need, through system design and data architecture, to deployment and ongoing reliability. You will work in close partnership with product leadership and ML scientists, but the technical decisions are yours to make and yours to own. The team model is intentionally lean. Each Principal Engineer on this team is a full technical lead for their domain, not one of several engineers on a layered team. You may from time to time coordinate the work of embedded software engineering contractors, but your primary contribution is your own engineering output and technical judgment. The VP of Engineering will set strategy and remove organizational blockers; the work itself is yours.

Responsibilities

• Own the technical architecture and implementation for one or more AI-powered product areas within the Pioneering Intelligence platform, including scientific workflow tools, data infrastructure, and agentic AI systems
• Design and build production-grade systems end-to-end: from data models and API contracts through deployment, observability, and long-term maintainability
• Engage directly with scientific, clinical, and ML users to understand what they actually need - not what they ask for - and translate that understanding into software that advances their work
• Make the highest-stakes technical decisions in your domain: system architecture, build-vs-buy tradeoffs, integration patterns for AI and ML components, and data pipeline design
• Collaborate closely with product and ML science counterparts, contributing technical perspective to product strategy and helping shape what gets built before a single line of code is written
• Participate in architectural reviews across the broader engineering team, raising the collective bar and identifying cross-cutting concerns before they become problems
• Use AI-native engineering tools - including Claude Code and similar - as genuine force multipliers in your daily practice, not as novelties
• When project needs require it, coordinate the work of fully embedded software engineering contractors, providing technical direction and maintaining quality standards
• Professional Experience and Qualifications
• Experiences
• Has served as the technical authority on a complex, production-grade system - not as a manager who delegates the hard parts, but as an engineer who owned the architecture and the most difficult implementation decisions
• Has built software that is actively used by researchers, scientists, or other domain experts to make consequential decisions, and understands how the needs of expert users differ from those of general operators
• Has designed and shipped systems that include ML or AI components - whether LLM-powered workflows, model inference infrastructure, or agentic pipelines - in a production setting
• Has operated effectively in ambiguous, fast-moving environments where requirements evolve and the right technical direction is not handed down from above
• Has worked closely with ML scientists or research teams to bridge the gap between experimental models and reliable production software

Requirements

• Technically credible at the system design level: can evaluate architectural options, identify failure modes in complex distributed systems, and make principled decisions about tradeoffs between reliability, performance, and maintainability
• Fluent in AI-native engineering: understands LLM capabilities and limitations, can design reliable orchestration layers for agentic workflows, and knows when AI is the right tool and when it is not
• Engineering craft: writes clean, well-tested, well-documented code and reviews others' work at a level that raises quality

Benefits

Additional Information

About Pioneering Intelligence Pioneering Intelligence builds on Flagship Pioneering's legacy of founding cutting-edge science and computational ventures, harnessing recent advances in AI, machine learning, and data to accelerate fundamental research and create a portfolio of AI-first companies. As part of Flagship's integrated model of science, entrepreneurship, and capital, it transforms breakthrough ideas into world-changing companies, elevating the AI advances happening across the ecosystem in human health, sustainability, and beyond.

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