Sr. Principal Software and AI Engineer
BeiGene
Summary
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.
Job description
Essential Functions of the Job
- Architect, build, and deploy AI and agentic powered applications that accelerate protocol generation and clinical trial execution, from study design through operational delivery.
- Partner with clinicians, medical writers, statisticians, data scientists, clinical operations, and product teams to embed AI into regulated clinical development workflows.
- Define scalable, reliable platform architecture for multi-agent orchestration, model and tool integration, and the full LLMOps lifecycle.
- Establish best practices for AI-native development, evaluation, testing, observability, and continuous performance monitoring within a validated SDLC.
- Assess frontier and open-weight models, agent frameworks, and tooling, and make sound build, buy, and model sourcing decisions.
- 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
- Bachelors with 7+ years, Masters with 5+ years or PhD with 3+ years
- 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.
- Proven success shipping production-grade AI and agentic applications that deliver measurable scientific or business impact, ideally in a regulated environment.
Core Technical Expertise
- 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.
- 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.
- Strong judgment in selecting and applying frontier and open-weight models, using structured outputs, guardrails, and validation to ensure stable, robust model behavior.
- Proven ability to design and manage enterprise knowledge architecture, including ontology design, knowledge graphs, and retrieval.
- Proven ability to build multi-layered evaluation, from expert benchmarks and rule-based checks to human review and model-based grading, backed by production monitoring.
- Fluency with AI coding tools and practices, applied within a disciplined SDLC with rigorous testing, code review, and CI/CD.
- Strong knowledge of LLMOps, secure cloud deployment (AWS, Azure, or GCP), and observability.
Clinical Development Domain
- Experience with clinical protocol development, study design, or clinical trial operations.
- Familiarity with ICH M11 structured protocols, CDISC standards, or USDM is a strong plus.
- Working knowledge of GxP, computer system validation, and emerging regulatory guidance on AI in drug development.
Leadership and Collaboration
- Proven ability to act as tech lead, owning product direction and delivery while mentoring senior engineers.
- Strong partnership with clinicians, medical writers, statisticians, and clinical operations, translating complex AI concepts for research, regulatory, and executive audiences.
- Deep commitment to data privacy, security, and responsible AI in development and deployment.
Requirements
- Bachelors with 7+ years, Masters with 5+ years or PhD with 3+ years
- Software and AI/ML engineering experience, including experience as a tech lead or principal engineer owning large-scale AI products end to end
- Proven success shipping production-grade AI and agentic applications that deliver measurable scientific or business impact, ideally in a regulated environment
- 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
- 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
- Strong judgment in selecting and applying frontier and open-weight models, using structured outputs, guardrails, and validation to ensure stable, robust model behavior
- Proven ability to design and manage enterprise knowledge architecture, including ontology design, knowledge graphs, and retrieval
- Proven ability to build multi-layered evaluation, from expert benchmarks and rule-based checks to human review and model-based grading, backed by production monitoring
- Fluency with AI coding tools and practices, applied within a disciplined SDLC with rigorous testing, code review, and CI/CD
- Strong knowledge of LLMOps, secure cloud deployment (AWS, Azure, or GCP), and observability
- Experience with clinical protocol development, study design, or clinical trial operations
- Working knowledge of GxP, computer system validation, and emerging regulatory guidance on AI in drug development
- Proven ability to act as tech lead, owning product direction and delivery while mentoring senior engineers
- Strong partnership with clinicians, medical writers, statisticians, and clinical operations, translating complex AI concepts for research, regulatory, and executive audiences
- Deep commitment to data privacy, security, and responsible AI in development and deployment
Benefits
- Medical, Dental & Vision
- 401(k)
- FSA/HSA
- Life Insurance
- Paid Time Off
- Wellness
- Stock Options
- Employee Stock Purchase Plan
Company overview
BeiGene, Ltd., now rebranding as BeOne Medicines, is a global commercial-stage biotechnology company specializing in the discovery, development, manufacturing, and commercialization of innovative oncology medicines. Founded in 2010 by John V. Oyler and Xiaodong Wang, the company has grown into a multinational organization with headquarters in Cambridge, Massachusetts, Beijing, China, and Basel, Switzerland. BeiGene focuses on developing targeted and immune-oncology therapies for cancer treatment, with a diverse pipeline that includes internally discovered products like BRUKINSA (zanubrutinib), a Bruton's tyrosine kinase inhibitor, and Tevimbra (tislelizumab), a PD-1 inhibitor. The company operates across six continents with over 45 countries participating in clinical trials and has enrolled more than 30,000 patients in its research programs. With approximately 12,000 employees worldwide, BeiGene has established itself as a significant player in global oncology, particularly with strong market presence in China and expanding operations in the United States and Europe. The company's mission centers on expanding access to high-quality cancer therapies worldwide through persistent innovation and challenging traditional approaches to drug development.