Artificial Intelligence Jobs in Cambridge, MA

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.

Design and run full-spectrum red team operations across corporate, lab, OT, cloud, and AI environments. Develop offensive toolchains, build scripts and exploits, execute end-to-end attack chains, emulate adversaries based on threat intel, and collaborate with IR, threat intel, and detection engineering to convert findings into durable detections and defenses.

Optimize and compress large language and vision models for on-device inference. Build distillation and hardware-specific compilation pipelines, benchmark performance across NPU/GPU architectures, and deploy models to edge environments.

Design and integrate vision encoders and audio-native models into agent reasoning loops, optimize streaming latency for voice-to-voice interactions, and architect multimodal RAG systems to retrieve insights from videos and PDFs while ensuring efficient cross-modal alignment.

Design, orchestrate, and optimize large-scale LLM pre-training runs across 1,000+ GPUs using PyTorch/DeepSpeed/Megatron-LM. Improve networking (InfiniBand/RDMA) and memory management, implement 3D parallelism, and automate checkpointing and failure recovery for month-long training jobs on SLURM or Kubernetes GPU clusters.

Develop intent-recognition models from gaze, gesture, and voice data; implement socially aware navigation algorithms respecting personal space; and design visual and auditory feedback for robot user interfaces. Conduct user studies and rapid prototype AI-driven voice and visual interaction systems to test and refine HRI paradigms.

Design and run adversarial tests on language and multimodal models, build guardrails and real-time filters for autonomous tools, and develop constitutional AI principles and RLHF alignment pipelines to ensure safe AI deployment.

Build and operate MLOps and agentic infrastructure: manage model registries, continuous training loops, and A/B testing; deploy agents as scalable microservices on Kubernetes; and create observability dashboards tracking token usage, latency, and agent reasoning.

Lead research developing foundation models for robotics, align text reasoning with low-level control, design zero-shot generalization experiments, integrate models into robotic applications, and publish and present results at conferences.

Design and build multi-agent orchestration frameworks, implement tool-calling pipelines and reasoning loops, and optimize latency and token usage for real-time agentic workflows.

Lead product strategy and roadmap to convert advanced AI and robotic research (VLA models, agentic RAG) into customer-facing solutions. Define safety, usability, and performance metrics, work with early enterprise adopters for feedback, and coordinate cross-functional teams to deliver complex hardware and deep-tech software products.

Lead and grow an AI Data Application team to build web services and platforms for metadata storage, dataset generation, and data search. Define technical vision, drive AI adoption in frontend UX, design APIs and query federation, collaborate with ML researchers and data teams, and mentor engineers to accelerate ML pipeline development.

The AI Solution Architect will design, implement, and optimize AI systems within the Bluefield framework, mentor engineers, and ensure architectural quality.

Lead the technical architecture and implementation for LangSmith, collaborating with teams, mentoring juniors, and ensuring code quality.

The Full Stack Engineer will develop and implement features for the LangSmith platform, collaborate with teams, ensure high coding standards, and engage with enterprise customers.

As a Senior Software Engineer, focus on backend development and AI integration for scientific applications, building systems that leverage advanced AI techniques.

Lead SimpliSafe's AI organization to define and execute company-wide AI strategy, own AI product roadmap, build cloud and on-device ML/CV platform, manage budget and hiring, partner with Product and Engineering, and translate cutting-edge research into consumer AI features for constrained devices like cameras.

Intern embedded with HR to identify repetitive HR tasks and build AI-powered automations using enterprise Copilot/Claude. Responsibilities include workflow mapping, roadmapping, data-governance reviews, prototyping, testing with synthetic data, documentation, and handing off maintainable tools to HR.

Build and maintain production-grade React+TypeScript web interfaces for data-intensive, real-time AI products. Own end-to-end frontend features, optimize UI performance for large datasets, collaborate with design and backend teams, establish frontend best practices, improve architecture and tooling, and resolve production issues across browsers and devices.

The Principal AI/ML Engineer will lead the design, implementation, and deployment of AI systems, mentor junior engineers, and ensure model performance and reliability in production environments.

Owner/implementer of end-to-end full stack features for data-intensive AI products: design and build scalable Node.js backends, REST APIs, and React+TypeScript frontends; optimize performance; establish testing, observability, and CI/CD; collaborate with product, design, and ML teams; debug and support production systems.

Design, develop, train, validate, and deploy AI/ML products for life-sciences customers. Advise leadership on technical and commercial feasibility, collaborate with cross-functional and commercial teams, and lead projects in RL, LLM agent harnesses, fine-tuning, governance, and model evaluation.

Lead and build an Applied AI organization that turns PSI's AI and physics research into customer-facing solutions. Hire and manage teams, scope and deliver engagements, integrate research and engineering, stay hands-on technically, and partner with sales to drive revenue while ensuring reusable, trustworthy deliverables.

Lead Core AI to build models, agents, and training systems that enable AI-driven physics discovery. Set roadmap, own research portfolio and infrastructure, hire and coach top talent, stay technical as a player-coach, and ensure results are scientifically trustworthy and production-ready for Applied AI and Engineering teams.