Top Natural Language Processing Jobs in Boston, MA

Build, modify, assemble, test, inspect, and troubleshoot electrical and electro-mechanical systems for unmanned aerial vehicles and integrated systems. Responsibilities include installing custom avionics and payloads, implementing hardware changes, fabricating wire harnesses, performing electrical tests, inspecting and reworking PCBs, isolating faults, and conducting functional checks. The role is onsite in Cambridge and requires U.S. citizenship and eligibility for a security clearance.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

Build and evaluate machine learning models, agentic systems, LLM applications, and pipeline components under senior-engineer guidance. Interns will gain experience across the ML lifecycle, including problem framing, model selection, development, evaluation, and deployment support. The role involves collaborating with ML Engineers, Product Managers, Designers, and Full-Stack Engineers on scalable AI solutions.

Lead AML/BSA, KYC/KYB, fraud, transaction monitoring, and sanctions compliance programs. Manage banking and financial institution relationships, oversee investigations and UAR/SAR processes, translate regulations into operational controls, coordinate audits and examinations, track regulatory developments, and deliver compliance training. Partner cross-functionally with Legal, Product, Engineering, and Operations to support responsible product growth and regulatory readiness.

Build AI-powered workflows, agents, integrations, and internal tools that automate People Operations. Partner with People, Engineering, and IT to modernize onboarding and other employee processes, while implementing secure access controls, human oversight, escalation paths, and production-ready architectures. Prototype with users, iterate quickly, and measure improvements in efficiency, employee experience, and operational consistency.

Develops and commercializes partner-built solutions across the Superhuman Suite. Responsibilities include shaping joint value propositions, creating pitch and demo assets, enabling alliance managers and partners, packaging repeatable solutions, coordinating launches, supporting partner growth, and tracking post-launch performance. The role works across Alliance Programs, Partner Marketing, Technical Alliances, Product, and solution architecture, with approximately 25% international travel.

Own the post-sale customer lifecycle for B2B SaaS accounts, including onboarding, implementation, adoption, value realization, account health, churn prevention, renewals, and retention. Lead customer trainings, manage integrations and configurations, identify risks, negotiate renewals, maintain Salesforce data, and collaborate with Sales, Product, Engineering, Support, and RevOps to improve customer outcomes and operational processes.

Develop product launch narratives and messaging that connect with customers and prospects. Create demos, case studies, webinars, campaigns, web content, and customer stories. Partner cross-functionally with product, sales, and customer success to ensure consistent positioning. Conduct customer and prospect research, identify market trends, and use data-driven insights to strengthen product marketing strategy.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

Lead an established RevOps and Enablement team to sharpen analytics, harden systems and data architecture, and scale AI-first workflows across Sales, Marketing, and CX. Own revenue tech stack, forecasting, GTM reporting, process optimization, and enablement programs while partnering with executive leadership to drive measurable revenue outcomes.