Top Tech Jobs & Startup Jobs in Boston, MA

Lead a software engineering team building privacy and data protection tooling across the data lifecycle. Define consumer-centric privacy solutions, partner cross-functionally, drive technical decisions, scale consent management, metadata catalog and protection controls, and measure risk mitigation and enablement outcomes.

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.

Design measurement frameworks and advanced causal inference solutions for marketplace allocation and recommendation systems. Partner with Product, Engineering, and business stakeholders to translate needs into scalable data science tools, run experiments, quantify impact, and drive implementation for Netflix discovery surfaces.

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.

Conduct research and develop identity and probabilistic-matching models for Netflix's ad-supported tier. Apply ML and statistical methods, ensure privacy-preserving solutions (differential privacy, anonymization), prototype and deploy production models, deliver datasets and tools, and partner with product, engineering, and business stakeholders to measure and drive impact.

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.

Lead and optimize Enterprise XD design operations: translate strategy into deliverables, drive large-scale programs, manage prioritization, resourcing, roadmaps, cross-functional partnerships, and advocate for rigorous design practices.