Top Natural Language Processing Jobs in Boston, MA

Build and improve ML platform tooling, infrastructure, workflows, and observability for machine learning models, LLMs, and agentic applications. Collaborate with ML and infrastructure teams, evaluate and integrate open-source tools, automate fine-tuning and evaluation pipelines, and debug distributed systems across infrastructure, networking, and application layers.

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.

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 AI transformation, governance, quality oversight, risk management, and responsible adoption across Pfizer Global Supply and QSEO. Establishes GxP-compliant AI lifecycle practices, inspection-readiness frameworks, governance forums, portfolio monitoring, inventory management, metrics, and reporting. Advises senior leaders on AI regulations, deployment risks, investment priorities, and compliance while leading cross-functional initiatives and influencing stakeholders across Quality, Digital, Operations, and R&D.

Build and operate Deepgram’s deal desk, including approval matrices, pricing playbooks, credit and promotion guidelines, non-standard deal reviews, pricing analysis, Salesforce CPQ workflows, and audit-ready approvals. Partner with Sales, Customer Success, Product, and Finance on pricing, packaging, renewals, and revenue recognition. Use AI for first-pass deal reviews and continuously improve desk rules and processes.

Build and operate high-throughput backend services for AI agents, calendar, scheduling, integrations, real-time data pipelines, and APIs. Design platform infrastructure, event-driven systems, and client-server protocols using Go, PostgreSQL, Kubernetes, and cloud platforms. Collaborate with AI, product, and frontend teams, improve reliability through testing and infrastructure as code, and mentor junior engineers.

Supports and maintains enterprise customer web integrations using Smartling’s web proxy solution. Owns technical tickets, troubleshoots browser and network issues, leads customer workshops, creates diagnostic scripts and tooling, manages production configuration changes, and collaborates with Engineering on product fixes. The role requires strong web technologies, debugging, communication, and customer-facing technical consulting skills.

Lead enterprise technical and business discovery, solution design, demonstrations, and proofs of concept for an AI platform serving financial institutions. Advise technical, security, compliance, and business stakeholders on AI architecture, integrations, and data handling. Develop reusable solution assets, identify recurring customer needs, support presales and post-sale delivery transitions, and provide feedback to Product and Product Marketing.

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.

Conduct a focused research project on agentic AI systems for applications such as robotics, vehicle design, factory logistics, or numerical optimization. Define project scope with a mentor, develop and evaluate AI methods, and work toward publication at a relevant research venue. The role requires foundation-model fine-tuning with reinforcement learning and use of agentic workflows to accelerate research experiments.