Top Natural Language Processing Jobs in Boston, MA

Develop, fine-tune, train, and optimize computer vision and machine learning algorithms. Manage experiments, data, testing, validation, and reproducibility using MLOps tools. Research emerging ML techniques, analyze field-test data, and transition algorithms into production across cloud, on-premises, and embedded environments. Collaborate with engineers and national security customers to deliver mission-critical AI and natural language processing capabilities.

Build and deploy production-grade AI and machine learning systems supporting biomedical research and drug discovery. Own technical solutions from architecture through deployment, including data pipelines, retrieval systems, knowledge systems, inference services, evaluation frameworks, and human-in-the-loop workflows. Partner with scientists and engineers to transform prototypes into scalable, secure, reliable platforms. Lead technical delivery, make architecture tradeoffs, improve engineering standards, mentor teammates, and promote responsible AI practices.

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 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.

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.

Own lifecycle email and in-app campaigns for portfolio products using Iterable and HubSpot. Build, schedule, test, and optimize customer journeys; coordinate cross-functional marketing projects; develop A/B tests; analyze campaign performance; improve execution systems and processes; and support cross-product adoption across Superhuman’s product suite.

Own complex technical support issues from reproduction through resolution, troubleshooting APIs, webhooks, workflows, integrations, and AI agent behavior. Escalate well-documented issues to Engineering, identify recurring patterns, and partner on product improvements. Create runbooks and troubleshooting guides, coach Tier 1/2 and BPO teams, support critical incidents and premier accounts, participate in on-call rotation, and handle sensitive customer data appropriately.

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.

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.

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.