Artificial Intelligence Jobs in Cambridge, MA

Develop and maintain high-quality technical manuals, guides, specifications, and related documentation. Lead information sessions with subject matter experts, align content with user needs and product requirements, improve existing materials, manage documentation projects, incorporate usability-testing feedback, and coordinate with design teams on visuals and diagrams. Apply documentation tools, industry standards, and best practices to improve clarity, usability, and content quality.

Owns end-to-end architecture for a major generative AI platform and shared AI services. Responsibilities include designing model integrations, APIs, data flows, orchestration, RAG and agentic workflows; defining architecture standards, integration contracts, and non-functional requirements; ensuring security, resilience, compliance, governance, observability, and scalability; and conducting architecture reviews and proof-of-concept assessments. The role partners with product, engineering, data science, cloud, security, and control teams to deliver production-ready enterprise AI systems.

Leads AI enablement, advanced analytics, automation, process mining, and digital transformation across Global Clinical Operations. Develops analytics assets, low-code/no-code solutions, workflow automations, reusable AI frameworks, and platform capabilities. Evaluates emerging technologies, defines business requirements, supports proofs of concept, promotes responsible AI, and drives adoption through communities of practice, super-user networks, coaching, and stakeholder engagement. Partners with clinical operations, enterprise technology teams, vendors, and global matrixed organizations to improve clinical trial execution and operational performance.

Lead design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, and evaluation. Collaborate with data and infra teams, drive architecture and inference decisions, and provide technical leadership and mentorship.

Senior AI Engineer will design and build cloud-native, agentic AI applications and platform patterns on AWS. Responsibilities include backend services (Python/FastAPI, TypeScript/Node), agent orchestration, retrieval and knowledge architectures, CI/CD and IaC, security and observability, and technical leadership to drive MVPs to production within agile AI Accelerator cycles.

Design, prototype, and ship machine learning systems for agentic commerce products. Conduct applied research from problem framing and data analysis through model training and evaluation, focusing on shopper and merchant agents, automated bot detection, and order management. Collaborate with engineering, product, and research teams to move concepts into production, stay current with AI advances, and communicate findings through documentation, demos, and publications.

Lead end-to-end product ownership for a suite of GTN AI/analytics products (Clarity, Compass, Dynamic Forecasting). Define vision, roadmap, and KPIs; prioritize use cases; drive delivery with cross-functional pods; manage vendors and platform selection; standardize and scale GTN workflows; ensure responsible AI, governance, and measurable business impact.

Support US Intermediary sales by transforming sales and market data into actionable insights. Lead analytical initiatives across product lines, identify growth opportunities, partner with sales leadership, and leverage AI and visualization tools to deliver executive-ready recommendations that influence targeting, campaigns, and distribution strategy.

The position focuses on technology and artificial intelligence at a multi-strategy investment firm, emphasizing innovation and collaboration.

Remotely operate and supervise robotic systems via teleoperation consoles, monitor live video and telemetry, perform real-time adjustments, follow procedures, collect and document operational data, identify anomalies, support data annotation and testing, and provide feedback to engineering teams for continuous improvement.

Remotely operate and supervise robotic systems via teleoperation consoles, monitor video and telemetry, execute tasks, collect and annotate operational data, report anomalies, and support continuous improvement with engineering teams.

Remotely operate and supervise robotic systems via teleoperation consoles, monitor video feeds and telemetry, execute real-time movements, capture operational data, document anomalies, support data annotation and quality validation, and provide feedback to engineering for continuous improvement.

Lead design and delivery of production AI Agent features for Veeva QualityDocs and Training. Define data flows, model integration, orchestration, and prompts; collaborate with engineering and UX to automate regulated quality and training processes; evaluate AI agents and present capabilities to stakeholders.

Lead design and delivery of AI agents for Veeva Quality Cloud to convert documents into structured data. Define data flows, orchestration, prompts, and model integrations; collaborate with engineering and UX; evaluate AI agents and present capabilities to stakeholders.

Serve as an AI-fluent implementation consultant for Veeva Vault Safety: advise on AI strategy, design validation and governance frameworks, build monitoring dashboards, run workshops, lead gap analyses and configurations, train customers, and ensure GxP/CSV compliance throughout implementations.

Conduct quantitative research to design and validate trading models, alpha signals, and analytics; apply statistical and ML methods to large financial datasets; develop research code and AI tools (Python, Matlab, SQL); collaborate with traders, SSA researchers, and academics to translate findings into scalable trading indicators and internal/client materials.

Designs and builds secure, production-grade GenAI and agentic AI applications using Python, LLMs, AWS, and Databricks. Develops scalable AI pipelines, integrates data and cloud services, applies RAG, vector databases, model evaluation, and guardrails, and creates cost-effective solutions. The engineer researches emerging tools, improves application infrastructure and performance, works independently, guides team members, and delivers under tight timelines.

Design and operate GPU/accelerator infrastructure and inference platforms for large ML models. Build Kubernetes-based scheduling, multi-tenant GPU sharing, autoscaling, model serving, IaC (Terraform/Helm) for EKS, CI/CD for model artifacts, observability for latency and throughput, and cost/capacity optimization across heterogeneous accelerators.

Apply deep learning and generative AI to digital pathology and spatial biology datasets to develop image-analysis algorithms, biomarkers, segmentation, cell phenotyping, and spatial feature extraction. Collaborate with pathologists and translational teams, validate and benchmark methods, publish results, and present at scientific forums to support oncology drug discovery.

Lead post-go-live optimization for Microsoft Dynamics 365 Supply Chain Management environments. Assess configuration and adoption across inventory, procurement, warehousing, planning, and related modules; identify business pain points; create enhancement roadmaps; guide system configuration changes; and apply AI and Copilot automation opportunities. The role also includes client and executive stakeholder engagement, relationship expansion, methodology development, mentoring, backlog management, and practice growth.

Leads the design, deployment, operation, and continuous improvement of secure enterprise AI platforms across Azure, AWS, and private clouds. Manages cloud services, Kubernetes, databases, LLM and AI/ML platforms, automation, infrastructure as code, CI/CD, monitoring, identity and access, incident response, resiliency, disaster recovery, governance, and compliance. Partners with engineering, security, data, risk, architecture, and business teams to deliver production-ready and responsible AI solutions.

As a Backend Engineer, you will design and build backend systems and APIs for LangChain's observability and evals platform, optimizing performance and reliability while collaborating with cross-functional teams.

Lead design, build, and productionalize generative AI applications and platform capabilities. Automate ML pipelines, optimize models (LoRA/QLoRA/PEFT), implement guardrails and monitoring, support RAG and vector DB tuning, mentor engineers, and collaborate with product and data teams to deploy safe, reliable GenAI features across web and mobile.

Lead 0→1 product development for Emerald Cloud Services: define vision, conduct customer discovery, set product requirements for AI compute provisioning and management, partner with engineering and GTM teams, and launch and scale cloud services for enterprise and AI customers.

Develop and drive AI strategies for clients by identifying AI use cases, conducting assessments, collaborating with cross-functional teams, and leading projects to deliver measurable business outcomes across sales, marketing, and customer experience.