Machine Learning Jobs in Cambridge, MA

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.

Lead a team of Principal Software Engineers to set technical strategy, contribute to system designs, and drive delivery while remaining technically active.

Sales role focused on growing and expanding enterprise accounts in New England. Own full sales cycle, hunt new logos, build pipeline, close deals, coordinate with BDR, solutions architecture, and marketing. Articulate business value of AI and networking/security solutions, use AI tools to enhance prospecting and forecasting, and meet aggressive activity and revenue targets.

Lead execution of quality strategy and ownership of the QMS for a machine-learning SaMD. Build and manage a quality team, guide software V&V and release processes, ensure global regulatory compliance (FDA, EU MDR), run audits, CAPA, risk and post-market activities, and partner cross-functionally with R&D, Regulatory, Clinical and Product to ensure safe, compliant product lifecycle.

Lead design, development, deployment, and maintenance of scalable AI/ML tools and reusable pipelines to accelerate target and biomarker discovery. Collaborate with computational biologists, data scientists, and engineers to scope data and build solutions, provide tactical and strategic ML engineering support, drive SDLC best practices for Python packages and Docker images, and propose innovative AI/ML approaches aligned with industry standards.

Lead statistical activities for Phase I-IV clinical trials from protocol design through reporting. Provide statistical oversight, collaborate cross-functionally, supervise programmers/CROs, apply SAS/R and ML/DL methods for analyses and automation, and support regulatory, safety, and publication deliverables.

Lead cross-functional oncology programs from discovery through preclinical development. Set scientific strategy, manage multidisciplinary teams, drive experimental and computational execution, ensure stage-gate readiness, mitigate risk, and develop scientific talent while aligning resources, timelines, and governance decisions.

Lead cross-functional cardio-renal-metabolic discovery programs from concept through preclinical proof-of-concept. Set scientific strategy, integrate biology and platform capabilities, drive stage-gate decisions, manage execution across wet-lab and computational workstreams, and ensure data quality and translational rationale. Build program plans, manage risks and budgets, represent programs in governance, and hire, coach, and develop scientific talent to deliver high-performing teams and stage-gate readiness.

Design and deploy production-grade ML systems, integrate models into software pipelines, and develop features leveraging ML for optimization and prediction problems.

Architect, develop, and integrate on-device and cloud AI/ML systems for surgical navigation. Lead model training, evaluation, and optimization; ensure regulatory traceability and V&V (IEC 62304/ISO 14971); collaborate across software, hardware, firmware, quality, and regulatory teams; mentor junior engineers and drive design/code reviews in Agile workflows.

Manage and conduct quantitative and qualitative research on sustainable and impact investing: build and maintain datasets, analyze data using ML/LLMs and traditional methods, draft publications and teaching materials, coordinate multiple projects and external partners, support research dissemination and events, and handle project operations and agreements.

As a Senior AI/ML Engineer, you will design and deploy LLM systems, collaborate with cross-functional teams, and manage model deployment in production environments.

Design, build, and maintain large-scale ETL/ELT data pipelines and cloud data architectures (AWS/Azure). Optimize Spark/Databricks/Synapse jobs, orchestrate workflows with Airflow, manage SQL/NoSQL databases, implement governance/security, create dashboards, integrate APIs, support ML model pipelines, and deploy using Docker/DevOps practices while monitoring pipeline health.

Design, build, and productionize motion planning software for mobile manipulators and AMRs; deploy, benchmark, and validate planners across fleet compute platforms; create simulation CI to catch regressions; debug and tune on real hardware at customer sites; collaborate with perception, hardware, firmware, and deployment teams; write production-grade Python and C++ software meeting strict reliability, latency, and safety requirements.

Lead the development of machine learning algorithms focusing on perception for autonomous driving, collaborating across teams and mentoring ML developers.

Lead design, build, and productionize scalable ML models and pipelines. Collaborate with product and data science teams, implement cloud-based architectures, ensure CI/CD, monitoring, model governance, and responsible AI practices.

Lead ML engineer responsible for designing, building, deploying, and monitoring production ML systems at scale. Collaborate with product and data science teams, develop ML architectures and data pipelines, apply CI/CD and cloud-based MLOps, retrain and govern models, and ensure performance, reliability, and responsible/explainable AI practices.

Lead an Agile team to design, build, deploy, and maintain production-scale ML models and pipelines. Drive ML architecture, model training, CI/CD, monitoring, and cloud/Kubernetes-based deployments while ensuring responsible, explainable, and well-governed ML solutions.

Build and deploy AI and machine-learning capabilities for cyber threat hunting, anomaly detection, incident response, detection engineering, and security automation. Develop custom models, LLM applications, and agentic systems, managing the full production lifecycle from data preparation through monitoring. Partner with security, engineering, offensive security, and governance teams to evaluate emerging technologies, support responsible AI, advise leadership, and mentor technical staff.

Lead development and deployment of AI-driven phenotyping pipelines using EMR and real-world data to define cohorts, extract features, and validate phenotypes. Integrate clinical, genomics, and biomarker datasets, build reproducible SQL/Python/R pipelines, apply statistical genetics and rare variant analyses, and improve data infrastructure to support Genetics-led programs.

Build and scale ML development infrastructure and Agentic AI tooling. Optimize distributed training (PyTorch/DDP), improve GPU utilization, design multi-cloud Kubernetes environments, and collaborate with ML researchers and engineers to streamline the ML lifecycle.

Lead the Accounting & Finance Reporting, Data & AI Center of Excellence: set strategy, governance, roadmap, and deliver analytics, automation, and AI solutions. Own intake, prioritization, implementation, adoption, and value realization across financial processes while partnering with IT and mentoring the team.

Lead client-facing, end-to-end AI strategy engagements: scope opportunities, design solutions, build roadmaps, and oversee implementation. Advise C-suite on AI adoption, value realization, governance, and technical enablement. Maintain hands-on fluency with modern AI stack and platforms, drive business development, mentor consulting teams, and contribute to an AI Center of Excellence.

Design, build, and deploy scalable ML pipelines for multimodal neuroscience data. Research, train, and deploy advanced deep learning and reinforcement learning models to predict brain states, detect pathological patterns, and generate personalized neuromodulation parameters. Select AI frameworks and cloud infrastructure, collaborate with computational neuroscientists, and contribute to IP and publications.

The Research Engineer/Scientist will advance decision-making in critical industries using optimization and machine learning, collaborating with product managers and engineers.