Top Tech Jobs & Startup Jobs in Boston, MA

Own and modernize end-to-end Workday-to-Talent integrations: design, build, deploy, and operate integrations; define standards; implement transformation logic; ensure observability, reliability, and vendor delivery; document and coach partners.

Lead and grow a team of ML scientists, data scientists, and analytics engineers to build bidding and pacing algorithms for Netflix Ads. Drive cross-functional partnerships with Product and Engineering, set roadmaps, ensure high-quality technical outputs, and translate business objectives into actionable analyses to optimize advertiser value and delivery across ad inventory.

Own metrics and datasets that measure cloud, storage, streaming, and ML infrastructure efficiency. Build end-to-end analytics: metric frameworks, pipelines, dashboards, and narratives that inform executive and engineering investment decisions. Pioneer methods to quantify AI and agent-driven productivity, identify efficiency opportunities, and partner with data engineers and data scientists to deliver trusted, reusable insights and recommendations.

Lead and deliver high-ambiguity, multi-team AI product programs from concept to production. Align engineering, applied science, product, and external partners; plan milestones, manage dependencies, and create decision-ready communications. Build lightweight program rituals, evaluate technical/business tradeoffs, and guide ML lifecycle execution for generative AI, search, discovery, and new content modalities.

Design, build, and maintain production-scale data systems and web-scale datasets (Spark/Flink) to support personalization ML. Drive dataset/schema design, collaborate with algorithm teams, and improve distributed big-data processing for member experience.

Build and prototype supervised ML models to predict ad campaign delivery outcomes (reach, frequency, contention). Own feature engineering, evaluation frameworks, interpretability, and partner with ML engineers to deploy and monitor models at scale. Collaborate across product, engineering, and sales to drive adoption and measure lift versus simulation baselines.

Own and improve the end-to-end developer experience for AIMS ML teams: local/remote dev environments, build/test infrastructure, CI/CD, and internal platforms. Design, build, and operate large-scale build and CI systems, remove developer friction, instrument workflows, drive adoption, and set technical standards. Partner with ML researchers to prioritize productivity investments and evaluate/integrate GenAI-powered developer tooling with safety guardrails.