Operator Accelerator Technology Program

The Product Engineer supports LGNDX teams by building and shipping customer-ready software end to end. This role is intentionally broad: the trainee will contribute to real projects while developing depth in one of three pathways—Full-Stack Product, Platform/Infrastructure, or ML/AI—based on project needs and the trainee’s strengths. Across pathways, the Product Engineer works closely with product, design, and engineering to deliver measurable outcomes with strong quality, reliability, and secure engineering practices.

Job description (core responsibilities for all pathways)-Build and ship features in an iterative cadence (tasks → PRs → review → demo)-Collaborate cross-functionally to clarify requirements and tradeoffs-Improve code quality and maintainability (tests, refactors, documentation)-Support delivery readiness (CI/CD hygiene, logging/metrics, issue triage)-Follow secure engineering practices (secrets, access, dependency hygiene)Pathway 1: Full-Stack Product Engineering -Implement UI flows, APIs, and service logic; improve UX and performance-Define and evolve data models; integrate auth/roles; build test coverage-Partner with design/PM to scope MVP slices and validate releasesPathway 2: Platform / Infrastructure -Improve environments, CI/CD pipelines, and deployment automation-Add observability (logging/metrics/tracing), alerts, and runbooks-Improve reliability, scalability, and operational readinessPathway 3: ML/AI-Enabled Engineering -Integrate ML/AI capabilities into product workflows (LLM APIs, retrieval, automation)-Implement evaluation and monitoring (test sets, quality checks, guardrails, fallbacks)-Improve data pipelines and production readiness (latency, cost, reliability)Qualifications -Experience building software in at least one area (web/app, APIs, cloud, data, or ML/AI)-Comfortable with Git, code review, and iterative shipping-Clear communicator who can collaborate with PM/design/engineering-Interest in learning across product + platform + AI-enabled systemsBonus: Cyber and RMF experience strongly desired, Docker/Kubernetes, IaC (Terraform), CI/CD, observability, or ML/LLM integration experience