Top Natural Language Processing (NLP) Jobs in Boston, MA

Design, develop, and productionize agentic AI systems and retrieval methods for financial data. Work across the ML lifecycle—experimentation, deployment, monitoring—and collaborate with product, data, and MLOps teams to build scalable, evaluated agent and NLP solutions.

Design and implement AI systems and scalable ML models (Python, TensorFlow); build/maintain data pipelines and integrations; apply NLP (NLTK) and deep learning for analytics; deploy and optimize models (including open-weight LLMs); collaborate with stakeholders and continuously learn to improve AI solutions and deployment reliability.

Lead and grow a team building Klaviyo's Customer Agent conversational AI platform. Set technical direction, own roadmap, collaborate cross-functionally, guide system and model design, ensure reliability, hire and develop engineers and managers, and deliver scalable, AI-driven customer engagement features.

The role involves partnering with cross-functional teams to design, develop, and deploy AI software solutions, focusing on large language models and AI optimization techniques.

Lead end-to-end design and execution of voice data programs: build ingestion pipelines, labeling specs, tooling, and quality safeguards; align data strategy with research and product; manage vendors and teams; track throughput, quality, and vendor performance; and prototype/deploy data tools to scale datasets that improve speech and voice AI model outcomes.

Own and evolve revenue calibration models tying CX interactions to revenue, design causal inference and experimentation frameworks, build LLM-powered classification/NLP pipelines, productionize models with Analytics Engineers, define segmentation and behavioral signals, and ensure statistical rigor for executive reporting and regulatory defensibility.

Design and implement Vercel's agentic interface and agent architecture to understand intent, plan workflows, select tools, recover from failures, and execute long-running tasks. Ship polished conversational and interactive experiences across Slack, web, and other surfaces. Build evaluation, observability, and feedback systems and primitives enabling teams to contribute consistent agent capabilities across the company.

Own end-to-end voice platform migration and scaling: LiveKit migration, real-time turn-taking, VAD, SIP/WebRTC, multi-agent handoff, training/fine-tuning voice models, inference cost optimization, and retrieval-based grounding using Supabase/pgvector.

Lead end-to-end performance of customer-facing conversational AI and knowledge base. Design, test, and optimize AI conversations, define intents and success metrics, analyze KPIs, maintain enterprise knowledge, run continuous improvement using analytics and experimentation, and partner cross-functionally to improve CX and business outcomes.

Lead data science and knowledge engineering initiatives to transform proposal, RFP, and due diligence content. Build NLP, semantic retrieval, and recommendation systems, design taxonomies and content governance, partner with stakeholders to improve first-draft quality and reuse, and measure impact through metrics like reuse rates, first-draft quality, SME touch frequency, and cycle-time reduction.