Artificial Intelligence Jobs in Cambridge, MA

The AI Platform Engineer will develop full-stack systems connecting AI models to real-world applications in drug design, collaborating with scientists and ensuring rapid experimentation cycles.

The Applied AI/Bio Scientist will utilize AI to design biologics, collaborate with teams, and improve discovery processes in therapeutic design.

The AI Product Owner guides AI engineering teams, ensuring technical excellence, mentoring engineers, overseeing solution design, and driving timely delivery of high-quality AI products.

Lead AI Engineer to mentor teams, guide AI solution design, ensure code quality, and manage complex AI projects while evaluating new technologies.

Lead the AI Engineering team to define technical architecture, mentor engineers, drive innovation, and ensure robust and scalable AI solutions.

As an AI Research Scientist II, you will develop and prototype AI solutions, advance drug design campaigns, and publish research while collaborating cross-functionally on cheminformatics and bioinformatics.

The AI Research Scientist III will prototype AI/ML solutions, advance drug design campaigns, mentor junior researchers, and publish scientific papers.

The AI Automation Engineer will automate internal processes, mentor non-technical staff on building tools, and ensure solutions deliver measurable business impact using AI technologies.

The AI Sales Specialist will develop sales strategies, engage clients, manage relationships, negotiate contracts, and provide insights on AI technologies to drive business value for enterprise clients.

The AI Solutions Architect engages in technical pre-sales, designing AI architectures, conducting technical discovery sessions, and collaborating on proof-of-concept projects while ensuring smooth transitions to delivery teams.

Lead AI ROI engagements end-to-end: build and defend financial business cases, convert attribution data into CFO-ready ROI and payback metrics, pre-register success criteria, partner with analysts and engineering to capture needed data, and productize repeatable methodology.

Research, design, and implement foundation models for machine-generated and graph data; develop distributed training and inference pipelines; collaborate with engineering and product teams to productionize models; evaluate new AI/ML techniques and drive MLOps, monitoring, and model optimization to improve scalability and reliability.

Lead on-device ML inference deployment and performance optimization for outdoor camera/doorbell workloads. Optimize latency, throughput, memory, power and stability across CPU/DSP/NPU/GPU. Perform kernel/operator-level tuning, runtime integration in C/C++, quantization validation, and build profiling/benchmarking tooling. Drive cross-functional work with ML, firmware, and hardware teams and provide staff-level technical leadership and mentoring.

Lead strategy, development, and production deployment of scalable AI/GenAI and ML solutions. Drive technical direction, mentor data scientists, own end-to-end initiatives, ensure MLOps and model governance, collaborate cross-functionally, and deliver measurable business impact using advanced models (LLMs, transformers, diffusion) and cloud platforms.

Design and build hands-on curriculum and lab environments teaching technical learners to build, configure, and deploy agentic AI on Veeva's platform. Create working AI agent demos and reference implementations, maintain advanced Java and REST API coding courses, partner with Product and Engineering, and apply instructional design to complex technical content.

Design, implement, and deploy end-to-end machine learning pipelines to optimize clinical trial operations. Collaborate with cross-functional teams to integrate AI solutions, manage AI product lifecycles, monitor post-deployment performance, and translate model outputs into actionable clinical operations strategies. Stay current on AI trends including NLP and predictive analytics.

Own the end-to-end lifecycle of LLM-powered agents and MCP tools: design, build, evaluate, test with customers, and deploy. Write and maintain evals, refine tool descriptions, analyze usage, track performance metrics, prioritize roadmap, and work hands-on in GitHub with engineering, intelligence, and design teams to ship reliable, safe agent/tool integrations.

The Research Manager, AI Safety will oversee research fellows, facilitate project execution, provide feedback, and manage partnerships related to AI safety initiatives.

The role involves program design for AI safety fellowships, supporting mentorship experiences, and improving research support systems for fellows.

Design and build LLM-powered agents for hardware product development. Responsibilities include ensuring effective coordination among engineering disciplines and integrating AI systems with existing tools.

Lead architecture, implementation, and operation of cloud-based AI/GenAI platforms and data lakehouse patterns. Own end-to-end delivery: ML lifecycle (MLflow), model serving, RAG/LLM systems, vector search, CI/CD, governance (Unity Catalog), and GxP-compliant validation. Mentor engineers, define standards, integrate enterprise data (SAP), and improve reliability, observability, and cost/performance.

The Software Engineer will develop and enhance infrastructure and user-facing functionalities in cloud-native simulation platforms, collaborate on technical projects, and support AI and backend system initiatives.

The Data Engineer will develop and optimize data pipelines for LLMs, ensuring high-quality healthcare datasets while collaborating with data scientists on model requirements.

The AI Operations Associate monitors AI-driven workflows, investigates incidents, supports operational controls, and recommends improvements to maintain workflow reliability and safety in finance environments.

Early-career Data & AI Engineer supporting client work and applied R&D. Prepare, clean, and validate heterogeneous data; build reproducible Python/SQL workflows; support ML and generative-AI tasks (embeddings, RAG, evaluation); prototype tools; document assumptions; participate in code review and translate project lessons into reusable assets.