Artificial Intelligence Jobs in Cambridge, MA

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.

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.

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.

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.

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.

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.

Design and build Machine Learning libraries, implement CI/CD pipelines, and support Ray-based compute environments, collaborating with ML experts.