Top Natural Language Processing Jobs in Boston, MA

Build and scale production-quality AI/ML systems for scientific and clinical workflows. Responsibilities include developing generative AI, agentic, predictive, foundation model, RAG, and fine-tuned systems; engineering data and ETL platforms; deploying cloud infrastructure and CI/CD; creating evaluation harnesses; debugging prototypes; assessing build-versus-buy options; establishing safeguards and SOPs; and collaborating with biologists, immunologists, computational scientists, and clinicians.

Lead technical vision and execution for autonomy and APNT solutions on unmanned aerial systems. Guide programs from research and development through flight testing and fielding, oversee multidisciplinary engineering teams, manage schedules and budgets, support systems integration, engage customers and partners, and identify new business opportunities. Provide technical oversight while driving successful delivery of mission-critical aerospace software and systems.

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.

Leads internal communications for a rapidly scaling, distributed technology company. Builds communication systems, channels, and cadences; partners with executives on employee messaging; translates complex business and technical information; supports culture, employee experience, company narrative, and major company moments; and uses data and AI to improve communication effectiveness.

Partner with enterprise financial firms to design and implement scalable practice management programs. Lead executive engagement, success roadmapping, content and curriculum development, and live facilitation. Drive adoption through program design, data-driven recommendations, and feedback to Product. Travel regularly to client sites and industry events to deliver sessions and represent Zocks as a subject-matter expert.

Lead full-cycle B2B sales growth across K12 and higher education markets. Manage pipelines, territory strategy, consultative selling, enterprise negotiations, RFPs, procurement, forecasting, and quota attainment. Coach sales teams, present AI-enabled education solutions to institutional leaders, navigate privacy and compliance concerns, and collaborate with Customer Success, Product, and Marketing.

Own end-to-end localization operations for enterprise clients, serving as the primary operational contact and trusted advisor. Coordinate stakeholders and cross-functional teams, manage localization workflows and configurations across TMS and project tools, resolve client and internal issues, and improve processes through documentation, automation, and AI-enabled solutions. The role requires localization or translation project management experience, client communication skills, familiarity with localization formats and CAT/TMS tools, and strong organizational abilities.

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.

Leads Data Quality Analysts supporting AI model evaluation and improvement. Owns quality across workflows through audits, calibrations, feedback loops, trend analysis, and stakeholder alignment. Translates evolving guidelines into team direction, manages operational performance, supports hiring, drives process and tooling improvements, handles complex decisions, and coaches team members. The role balances quality, delivery, resource management, and continuous improvement in a human-in-the-loop AI environment.