Artificial Intelligence Jobs in Cambridge, MA

Lead system-level architecture and detailed design of ultra-low-power analog and digital AI accelerators. Translate product requirements into block-level specs, explore novel intelligence representations, develop system models with Synopsys Platform Architect, inform RTL and verification, and collaborate across compute IP, firmware, physical design, and partners while providing technical leadership and mentorship.

The Investment AI Engineer will design, build, and operate AI workflows for investment processes, collaborating with researchers and portfolio managers to improve automation and efficiency.

Founder role to build a B2B SaaS company focused on AI compute economics and GPU/ML infrastructure. Partner with Forum Ventures for funding, GTM, product and hiring support. Validate product-market fit, lead engineering and go-to-market, and raise seed/Series A. Must have domain expertise in GPU/cloud economics or ML platform, attract and lead teams, and operate in North American time zones.

Lead design, development, and scaling of a production-grade, multi-tenant AI agent platform. Own technical roadmap, architecture, reliability, observability, and developer experience. Build and mentor a team of platform engineers, drive multi-agent orchestration, LLM integration, security, and cross-functional delivery for enterprise-scale AI workflows.

Lead North America Commercial AI strategy and deliver production AI/LLM solutions (RAG, agentic workflows, predictive models) for patient finding, HCP engagement, next-best-action, and commercial operations. Partner with Commercial, Data Science, IT, Legal, and Compliance to prioritize use cases, evaluate vendors, productionize models, enforce responsible AI and monitoring, and measure business impact and adoption.

Lead architecture and execution of an enterprise AI platform on Azure: define multi-year roadmap, model lifecycle and evaluation standards, drive multi-LLM orchestration, enforce governance and compliance, build monitoring/observability and cost-optimization, manage identity and security controls, and enable firmwide adoption and executive reporting across Tax, Audit, Advisory, and Outsourcing.

Lead AI, automation, and Microsoft 365-driven digital transformation to streamline workflows, reduce costs, design scalable business systems, and build an AI learning institute. Partner across teams, manage cross-functional projects using Agile, ensure privacy/compliance, and deliver training to increase AI literacy and adoption.

Lead legal support for product development, AI governance, IP and global privacy. Advise product, engineering, and design teams; manage global IP portfolio; run privacy compliance (GDPR, CCPA/CPRA); draft and negotiate data processing and privacy terms; manage outside counsel and legal budgets; deliver training and cross-functional guidance to reduce risk and enable product innovation.

Perform disciplined outbound prospecting and run the full pre-sales cycle from cold outreach to qualification and initial deep dives. Create and advance commercially meaningful AI/SaaS opportunities, engage senior stakeholders and economic buyers, articulate Quantanite's AI-enabled value proposition, and help convert high-value deals while maturing into a full Account Executive within six months.

Design, implement, debug, and optimize neuro-symbolic and deep learning AI systems; collaborate with engineers and subject-matter experts; lead R&D programs; write reports and papers; engage with customers and manage deployments.

Design and deliver AI Agent solutions for Veeva Labeling, build prototypes and production features (data flow, orchestration, prompts), integrate models, collaborate with engineering and UX, evaluate AI agents, and present capabilities to stakeholders. Requires stakeholder communication and independent work in a fast-paced environment.

Drive adoption and growth of Agentforce, Data Cloud, and the Salesforce Platform by partnering with customers to align AI and data capabilities to business objectives. Lead consultative discovery, engage business and technical stakeholders, navigate commercial and enterprise buying cycles, and collaborate with internal teams to deliver multi-cloud AI solutions that expand platform adoption and measurable AI-driven outcomes.

Lead technical discovery and demos for enterprise prospects, translate pain points into LLM-powered solutions, present architecture and value, and feed customer insights to Product, Marketing, and R&D to shape the platform and sales strategy.

Own and operate OnRamp's AWS platform and deployment systems, build CI/CD and agent infrastructure, apply LLMs and coding agents to automate DevOps, ensure observability, reliability, security and compliance (SOC 2/HIPAA), and create developer-facing automation to increase team velocity.

Lead research and design of large-scale deep learning systems for protein structure prediction and molecular glue design. Develop novel architectures and training paradigms, collaborate with chemistry/biology experts, publish at top ML venues, and apply models to drive drug discovery value.

Lead development and deployment of AI-enabled biomedical image analysis workflows. Partner with wet-lab scientists to design, benchmark, validate, and scale segmentation, representation learning, and phenotypic profiling methods. Drive adoption of foundation-model and embedding-based approaches, ensure reproducibility and integration with platform engineering, and communicate results to scientific stakeholders to support drug discovery decisions.

Design, build, and productionize end-to-end AI-enabled applications and agentic workflows. Own front-end, back-end, APIs, data access, orchestration, integrations, deployment, monitoring, and operational quality. Iterate with stakeholders, define governance and best practices, and ensure secure, reliable, scalable AI solutions.

Design, build, and productionize end-to-end AI-enabled full-stack applications and agentic workflows. Collaborate with stakeholders to iterate on real use cases, integrate with enterprise systems, ensure security and observability, and improve agent quality through testing, evaluation, and operational metrics.

Own end-to-end dealer implementations for an AI-driven SaaS product: configure integrations, validate data flows, train non-technical staff, manage go-live timelines and stakeholders, troubleshoot live issues, write runbooks/SOPs, and route field feedback to product and engineering.

Lead and transform the US pitch and pursuit process by embedding AI enablement across stages. Manage a searchable global pitch content library, maintain structured experience and CRM data, create reusable pursuit assets and prompt frameworks, and develop training and adoption materials. Partner with BD, lawyers, and global teams to improve workflow, support high-priority pitches, and oversee directory and awards submissions to increase speed, quality, and consistency of wins.

The Translational Post Doctoral Researcher will integrate multi-modal neuroscience data using AI, developing evaluation frameworks for drug discovery. Responsibilities include building data pipelines, assessing AI quality, and publishing findings.

Build and architect agentic, LLM-powered systems end-to-end. Lead full-stack development (front-end, back-end, infra), design AI-native UX, prototype with co-founders and customers, define technical foundations, and iterate quickly in a zero-to-one startup environment.

Own and govern Microsoft Power Platform as a CoE lead, design and deliver AI-first automation (Power Apps, Power Automate, Copilot Studio), integrate with enterprise systems, enable citizen developers, monitor platform health, and ensure GxP-compliant operations and ALM for production-grade automations.

Lead architecture, selection, and implementation of non-human identity and endpoint execution controls (Windows/macOS). Build an AI security control plane, manage secrets/credential lifecycles, run vendor evaluations/POVs, lead a small senior engineering team, integrate controls into developer workflows, and partner with SecOps on detection and incident response.

Administer, govern, and scale Microsoft Power Platform and AI automation across enterprise environments. Design and develop Power Apps, Power Automate flows, Copilot Studio agents, and integrations (Microsoft 365, Dataverse, APIs). Enable CoE adoption through training, governance, monitoring, ALM, and citizen-developer support while ensuring compliance, validation, and operational excellence in regulated settings.