Sr. Principal Software and AI Engineer

BeOne continues to grow at a rapid pace with challenging and exciting opportunities for experienced professionals. When considering candidates, we look for scientific and business professionals who are highly motivated, collaborative, and most importantly, share our passionate interest in fighting cancer.

General Description:

We are seeking a Sr. Principal Software and AI Engineer to serve as the senior technical authority for AI-powered applications across drug development, with a primary focus on protocol generation and clinical trial execution. In this hands-on senior technical leadership role, you will help shape the R&D AI strategy and set technical direction for the engineering team, while building critical components yourself and setting best practices for AI-native development within a disciplined, validated SDLC. You will architect and ship production-grade agentic applications and platforms, set standards for evaluation, observability, and GxP compliance, and raise the bar for engineers through design reviews and mentorship.

Essential Functions of the Job:

• Build AI and Agentic Applications: Architect, build, and deploy AI and agentic powered applications that accelerate protocol generation and clinical trial execution, from study design through operational delivery.
• Collaborate Broadly: Partner with clinicians, medical writers, statisticians, data scientists, clinical operations, and product teams to embed AI into regulated clinical development workflows.
• Shape Architecture: Define scalable, reliable platform architecture for multi-agent orchestration, model and tool integration, and the full LLMOps lifecycle.
• Set Standards: Establish best practices for AI-native development, evaluation, testing, observability, and continuous performance monitoring within a validated SDLC.
• Drive Innovation: Assess frontier and open-weight models, agent frameworks, and tooling, and make sound build, buy, and model sourcing decisions.
• Ensure Compliance: Uphold GxP, data privacy, security, and responsible AI standards, with traceability, auditability, and human oversight built into every system.

Supervisory Responsibilities:

• Act as tech lead with full ownership of product development, accountable for technical direction, delivery, and quality from concept through production.
• Lead project execution, setting priorities, scope, and timelines for the AI engineering team.
• Mentor AI and software engineers through design and code reviews, reference implementations, and hands-on guidance.
• Lead cross-functional technical workstreams with clinical, medical writing, biostatistics, and clinical operations partners.

Foundational Background

• Education: Bachelors with 7+ years, Masters with 5+ years or PhD with 3+ years
• Experience: Software and AI/ML engineering experience, including experience as a tech lead or principal engineer owning large-scale AI products end to end. Experience in the pharmaceutical or life sciences industry is strongly preferred.
• Track Record: Proven success shipping production-grade AI and agentic applications that deliver measurable scientific or business impact, ideally in a regulated environment.

Core Technical Expertise

• Full-Stack Software Engineering: A strong software engineer first. Deep backend expertise in Python (preferred) and/or .NET, with a track record of architecting and building enterprise applications end to end across frontend, APIs, databases, and deployment. Hands-on experience with relational, search, and graph data stores such as PostgreSQL, Elasticsearch, and Neo4j. Sound judgment in system design, data modeling, and production operations.
• Multi-Agent, AI-Native Applications: Deep, hands-on experience architecting and shipping multi-agent applications to production, with strong command of agent harness design, context engineering, orchestration, tool integration (including MCP), and human-in-the-loop controls.
• Frontier Model Application: Strong judgment in selecting and applying frontier and open-weight models, using structured outputs, guardrails, and validation to ensure stable, robust model behavior. Experience with fine-tuning or post-training is a plus.
• Knowledge Engineering: Proven ability to design and manage enterprise knowledge architecture, including ontology design, knowledge graphs, and retrieval, to make domain knowledge structured, governed, and usable by AI systems.
• **Evaluation and Re…