Top Tech Jobs & Startup Jobs in Boston, MA

Design and build scalable training and inference systems for LLMs and multimodal/media models. Optimize end-to-end training (data pipelines, distributed training, mixed precision) and inference (KV cache, batching, quantization). Scale models into production, lead efficiency initiatives, and mentor engineers and scientists to improve latency, scalability, and reliability.

Lead two teams (Consent Data & Privacy and User Data Management) as a manager-of-managers, owning execution and multi-year technical vision for user consent, data collection, storage, pipelines, access, retention, and compliance. Partner with Product, Data Engineering, and Legal to enforce consent and data governance at scale, adapt to evolving regulations, and drive architecture and platform solutions.

Lead product strategy and execution for a portfolio of talent systems (first-party and vendor platforms). Partner with engineering, design, and data science to understand talent data flows, build AI-driven HR products, prioritize roadmaps, and drive launches, adoption, and continuous improvement across the employee lifecycle.

Lead product for Data Developer and Analytics Experiences, defining strategy, roadmaps, and delivery for SQL/Python authoring, semantic layer, developer tooling, visualization, governance, and GenAI integrations to improve productivity and reuse across Netflix analytics.

Design and execute ML research and end-to-end model development for generative and summary-generation systems. Lead rigorous experiments, translate findings for stakeholders, partner with engineers to deploy models, track state-of-the-art advances, and define roadmaps to improve promotional writing and discovery for members.

Build and operate next-generation ML data and feature infrastructure, including near-real-time feature computation engines, feature stores, and embedding retrieval systems. Scale data processing, storage, and serving for high-throughput training and low-latency inference. Collaborate with ML practitioners and AI Platform teams to improve feature discovery, sharing, and practitioner productivity.

Design and lead qualitative studies (interviews, focus groups, ethnography, thematic analysis) to deepen employee listening. Partner with quantitative researchers and Talent stakeholders to synthesize insights, build mixed-methods narratives, and scale culturally informed qualitative research across a global organization to influence HR decisions.

Technical lead for a Context Enablement pod building the context layer for coding agents: plugins, Dev Profiles, and a curated knowledge system (NECP). Define technical strategy, partner with platform teams, ship incremental production work, measure adoption, and mentor a team of engineers to improve developer productivity with agent-enabled tools.

Design, build, and operate highly reliable distributed storage services, APIs, and tooling at cloud scale. Engage with engineering users, solve large ambiguous problems incrementally, and improve platform observability, cost, and lifecycle management.

Build and maintain scalable batch and real-time data pipelines, model data to power analytics and ML, and operate distributed data systems. Collaborate with business, engineering, and data science teams to ensure data quality, documentation, and debugging while adopting new technologies and working in an ambiguous, fast-changing environment.