Top Tech Jobs & Startup Jobs in Boston, MA

Leads direct-to-consumer commercial planning, revenue forecasting, performance management, executive reporting, and operating cadence. Partners with Growth Marketing, Finance, Supply Chain, and Analytics to evaluate performance, prioritize investments, develop pricing and promotional strategies, and translate analysis into actionable recommendations. Drives cross-functional initiatives from strategy through execution, improves forecasting methodologies and planning processes, and influences senior stakeholders without direct authority.

Leads end-to-end manufacturing new product introduction programs for consumer hardware, from prototype through mass production. Owns readiness across EVT, DVT, PVT, pre-production, and ramp; coordinates engineering, quality, supply chain, operations, suppliers, and contract manufacturers. Develops schedules, risk registers, issue tracking, validation plans, and operating cadences while driving factory readiness, process validation, yield, capacity, equipment deployment, and cross-functional issue resolution. The role is Boston-based and requires international travel up to 25%.

Own monetization strategy and roadmap across pricing, promotions, upsells, membership plans, bundles, and business models. Analyze customer behavior, funnel performance, unit economics, and financial outcomes to identify opportunities and prioritize investments. Lead product discovery, delivery, experimentation, and iteration with Engineering, Design, Analytics, Finance, and Marketing. Develop hypotheses, run A/B tests, and translate insights into scalable experiences that improve conversion, revenue, retention, lifetime value, and growth.

Leads product analytics across core product, hardware, and algorithms while managing and developing analysts. Partners with cross-functional teams to define KPIs, evaluate experiments, analyze member behavior and product performance, build scalable analytics processes, and influence product strategy through actionable insights. The role requires hands-on analysis, advanced SQL, data warehousing expertise, strong storytelling, and the ability to guide senior stakeholders and technical teams.

Leads WHOOP’s cloud-based machine learning organization responsible for sleep, recovery, and training algorithms. Manages ML scientists and engineers, establishes technical standards and development practices, guides architecture and deployment, and partners with platform, hardware, sensor, product, research, and engineering teams. The role requires scaling production ML systems, improving model validation, monitoring, and deployment, and translating physiological sensor capabilities into reliable consumer experiences.

Analyze lifecycle marketing communications across channels, segments, and lifecycle stages to identify engagement, monetization, and retention insights. Partner with Lifecycle Marketing stakeholders, design and evaluate A/B tests, build dashboards and reports, develop data models, and present recommendations that improve targeting, timing, content, and member experience.

Build and maintain backend infrastructure and member-facing generative AI features that deliver personalized health and performance insights. Collaborate with Product, Design, Analytics, and Data Science teams to define requirements and deliver scalable solutions. Develop reliable software using Java, Kafka, AWS, and PostgreSQL; design RESTful APIs and relational databases; optimize performance; and maintain observability and monitoring systems.

Lead research, development, and deployment of large-scale multimodal foundation models integrating wearable, language, biomarker, clinical, and behavioral data. Develop self-supervised and representation-learning methods, distributed training pipelines, and post-training techniques. Collaborate with engineering, MLOps, data, product, and research teams to operationalize robust, observable, privacy-preserving models and translate capabilities into member-facing features. Contribute to technical roadmaps and architectural decisions for WHOOP’s foundation AI systems.

Own manufacturing technical readiness and end-to-end NPI execution for two concurrent consumer electronics programs. Lead process development, validation, optimization, factory troubleshooting, fixture and equipment qualification, automation decisions, and production handoff. Manage risks, milestones, supplier readiness, and cross-functional communication while applying DFX and Lean Six Sigma. Lead and mentor a small distributed manufacturing engineering team, balancing hands-on factory execution with delegation and technical prioritization.

Leads and manages an AI Quality engineering team responsible for evaluating and improving AI outputs. Defines testing frameworks for model performance and drift, establishes technical strategy and architecture, provides technical direction, manages technology vendors and partnerships, drives engineering quality improvements, and mentors team members.