Sr. Software Engineer, Enterprise AI Enablement Engineer

ModernaTX • Cambridge, MA

18h • Onsite

About The Position

The Sr. Software Engineer, Enterprise AI Enablement Engineer will turn high-value AI prototypes into secure, reliable, scalable products. As an advanced individual contributor within Enterprise AI Enablement, you will partner with scientific, business, product, platform, data, and security teams to take early-stage applications through architecture, migration, evaluation, deployment, and transition to durable ownership. You will provide technical leadership across the application, data, cloud, and AI layers while establishing reusable engineering patterns that help Moderna move from experimentation to production more quickly—without sacrificing quality, security, traceability, or cost discipline.

Requirements

• Bachelor’s degree in Computer Science, Software Engineering, Bioinformatics, Computational Science, Data Science, or a related discipline, or equivalent practical experience.
• At least 5 years of professional software, data, or computational engineering experience, including at least 2 years working with machine learning, LLM, or agentic AI applications.
• Strong programming skills in Python and experience with TypeScript, JavaScript, or another modern application-development language.
• Demonstrated experience designing modular services and APIs, working with relational databases and data models, and applying automated testing, version control, CI/CD, containerization, and cloud engineering practices.
• Hands-on experience with LLM or agent workflows, including model APIs, tool or function calling, prompt and context design, structured outputs, evaluation, and failure-mode testing.
• Experience converting prototypes into maintainable products, including dependency management, environment capture, observability, documentation, deployment, and operational handoff.
• Proven ability to troubleshoot complex application and data-platform problems, identify root causes, evaluate architectural tradeoffs, and deliver durable improvements.
• Ability to lead technical work across cross-functional teams, communicate effectively with technical and nontechnical stakeholders, and manage competing priorities with limited supervision.

Nice To Haves

• Master’s degree or PhD in Computer Science, Bioinformatics, Computational Biology, Data Science, Engineering, or a related field.
• Experience developing software, computational platforms, or AI-enabled workflows within biotechnology, life sciences, research, or another scientifically complex environment.
• Experience with AWS or comparable cloud platforms, high-throughput data processing, workflow orchestration, and technologies such as PostgreSQL, Redshift, Athena, or their equivalents.
• Experience building reusable agent infrastructure, model-provider abstractions, evaluation frameworks, contextual retrieval systems, or standardized application-development harnesses.
• Familiarity with enterprise and open-weight language models and the performance, security, cost, and operational considerations involved in selecting and deploying them.
• Experience operating in regulated or quality-sensitive environments, including secure data handling, provenance, auditability, architecture review, and validation practices.
• A record of creating reference patterns, mentoring other builders, influencing engineering practices across teams, and delivering measurable improvements in production readiness, reliability, or adoption.

Responsibilities

• Lead the end-to-end productionization of agentic AI applications, from technical discovery and architecture through development, testing, deployment, stabilization, and operational handoff.
• Assess and refactor early-stage applications into modular, maintainable services, APIs, user interfaces, and data models while preserving business intent and feature parity.
• Provide technical direction across multiple concurrent initiatives, managing dependencies, risks, platform constraints, and delivery tradeoffs with limited oversight.
• Create reusable agent skills, migration playbooks, reference architectures, evaluation harnesses, and deployment patterns that accelerate delivery across related teams.
• Design and evaluate LLM and agent workflows, including tool use, structured outputs, contextual retrieval, model selection, failure testing, and appropriate boundaries between deterministic software and model-based reasoning.
• Diagnose complex issues across application, data, platform, and infrastructure layers using logs, metrics, test harnesses, and systematic root-cause analysis; automate solutions to recurring problems.
• Embed security, privacy, data classification, traceability, and compliance requirements throughout the engineering lifecycle, partnering with the appropriate review teams when needed.
• Produce high-quality architecture documents, technical specifications, API contracts, test plans, runbooks, and handoff materials that support reproducibility and long-term ownership.
• Define and track meaningful measures of engineering impact, including time to production, reliability, throughput, reuse, adoption, operational cost, and reduction of manual effort.

Benefits

• Competitive healthcare, plus voluntary benefit programs to support your unique needs
• A holistic approach to well-being, with access to fitness, mindfulness, and mental health support
• Family planning benefits, including fertility, adoption, and surrogacy support
• Generous paid time off, including vacation, volunteer days, sabbatical, global recharge days, and a discretionary year-end shutdown
• Savings and investments to help you plan for the future
• Location-specific perks and extras

What This Job Offers

• Job Type: Full-time
• Career Level: Senior