Sr. Principal Software and AI Engineer

Accountabilities

Lead AI application development: Architect, develop, and deploy production-ready AI-powered and agentic applications that support clinical protocol generation, study design, and clinical trial operations.
Define technical strategy and architecture: Establish scalable and reliable architectures for multi-agent orchestration, model integration, tool connectivity, and the complete LLMOps lifecycle.
Drive end-to-end product delivery: Take ownership of technical direction, project execution, priorities, scope, timelines, and software quality from initial concept through production deployment.
Establish AI engineering standards: Define and implement best practices for AI-native development, testing, evaluation, observability, continuous monitoring, and CI/CD within a rigorous software development lifecycle.
Advance AI capabilities: Evaluate emerging foundation models, open-weight models, agent frameworks, and development tools to make informed decisions about technology selection, model sourcing, and build-versus-buy strategies.
Develop enterprise knowledge systems: Design and maintain knowledge architectures, including ontologies, knowledge graphs, and retrieval systems that make complex scientific information structured, governed, and accessible to AI applications.
Ensure AI reliability and compliance: Implement evaluation frameworks combining expert benchmarks, rule-based validation, human review, and model-based assessments. Integrate data privacy, security, auditability, traceability, responsible AI practices, and human oversight into production systems.
Collaborate across disciplines: Partner with clinical, medical writing, biostatistics, data science, product, and clinical operations teams to identify opportunities and integrate AI into regulated clinical development workflows.
Mentor and develop engineering talent: Guide engineers through architecture discussions, code reviews, reference implementations, technical coaching, and hands-on collaboration while promoting engineering excellence.
Improve operational performance: Apply AI-assisted development tools and modern software engineering practices to increase developer productivity without compromising testing rigor, maintainability, or production quality.
Requirements
Education: Bachelor's degree with at least 7 years of relevant experience, a master's degree with at least 5 years of experience, or a Ph.D. with at least 3 years of experience.
Professional experience: Extensive software engineering and AI/ML engineering experience, including principal-level or technical leadership responsibilities and ownership of large-scale AI products from concept through deployment.
Production AI expertise: A demonstrated track record of delivering reliable, production-grade AI and agentic applications with measurable scientific or business impact. Experience in pharmaceutical, biotechnology, life sciences, or other regulated industries is strongly preferred.
Full-stack software engineering: Strong backend development expertise in Python and/or .NET, with experience building enterprise applications across frontend interfaces, APIs, databases, and deployment environments.
Data architecture: Hands-on experience with relational, search, and graph databases, such as PostgreSQL, Elasticsearch, and Neo4j, alongside strong capabilities in system design, data modeling, and production operations.
Agentic AI and LLMs: Deep practical knowledge of multi-agent application development, agent harness design, context engineering, orchestration, tool integration—including Model Context Protocol (MCP)—and human-in-the-loop controls.
Model integration and optimization: Ability to select and apply frontier and open-weight models, implement structured outputs and guardrails, and validate model behavior. Experience with fine-tuning or post-training techniques is an advantage.
Knowledge engineering: Experience designing enterprise knowledge architectures, ontologies, knowledge graphs, and retrieval systems that support reliable AI-driven reasoning and information access.
AI evaluation and observability: Proven ability to develop layered evaluation strategies using expert benchmarks, rule-based checks, human assessments, model-based grading, and production monitoring.
Cloud and development infrastructure: Strong understanding of LLMOps, secure cloud deployment across AWS, Microsoft Azure, or Google Cloud Platform, and modern observability practices.
AI-assisted software development: Familiarity with AI coding tools and workflows, combined with disciplined testing, code review, continuous integration, and continuous delivery practices.
Clinical and regulatory knowledge: Experience in clinical protocol development, study design, or clinical trial operations is desirable. Familiarity with ICH M11 structured protocols, CDISC standards, USDM, GxP requirements, computer system validation, and emerging AI regulations in drug development is a strong advantage.
Leadership and communication: Proven ability to lead technical workstreams, mentor senior engineers, influence architectural decisions, and communicate complex technical concepts clearly to scientific, regulatory, business, and executive stakeholders.
Professional competencies: Strong analytical thinking, problem-solving, initiative, adaptability, collaboration, and commitment to data privacy, information security, responsible AI, and continuous improvement.
Travel: Willingness to travel approximately 10% of the time.
Benefits
Competitive compensation: Annual base salary ranging from $137,400 to $182,400, depending on qualifications, experience, skills, and work location.
Annual incentive opportunities: Eligibility for an annual bonus plan for non-commercial roles.
Equity participation: Opportunity to receive discretionary equity awards and participate voluntarily in an employee stock purchase plan.
Comprehensive healthcare: Medical, dental, and vision insurance options.
Retirement savings: 401(k) plan to support long-term financial planning.
Flexible spending options: Access to flexible spending accounts (FSAs) and health savings accounts (HSAs), subject to eligibility.
Insurance protection: Life insurance and other applicable employee protection benefits.
Paid time off: Paid leave to support personal needs, rest, and work-life balance.
Wellness support: Access to wellness benefits and programs.
Remote work: Remote position based in the United States, offering flexibility in how work is organized.
Professional impact and growth: Opportunity to shape advanced AI engineering practices, mentor talented professionals, and contribute to technologies that can improve drug development and patient outcomes.