Machine Learning Jobs in Cambridge, MA

Develop and implement quantum error correction protocols and decoding algorithms, collaborating with academic and industry partners on a Rydberg atom quantum computer.

Develop advanced Vision-Language-Action models for robotaxis, leading data strategies and improving driving behaviors using machine learning infrastructure.

Design, train, and deploy machine learning models for RF signal detection, classification, and vessel activity tracking in maritime intelligence.

The role involves developing machine learning algorithms, optimizing LLM deployments, and managing deployment pipelines while mentoring team members.

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.

The Machine Learning Scientist will build and integrate ML models, analyze data, design scalable pipelines, and ensure robust model testing.

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

Develop and integrate detection, tracking, classification, and scene-understanding ML models for on-vehicle perception. Define and implement on-vehicle tracking architecture, collaborate with model and data teams, optimize models for high-performance vehicle deployment, and work with perception input/output data.

Design and develop auto-labeling algorithms for autonomous driving, collaborating with ML engineers to enhance data quality and efficiency in model training.

Lead development and production deployment of ML products: perform exploratory analysis, build and evaluate models, design experiments and metrics, leverage generative AI and graph-based methods, collaborate with product and engineering, influence strategy, and maintain robust CI/CD and platform practices.

Designs, implements, and deploys cloud-native AI/ML solutions and data pipelines to support personalization and optimization. Builds and serves ML models (real-time and batch), develops APIs and front-end tools, automates workflows, and ensures secure, scalable AWS infrastructure. Leads architecture discussions, mentors engineers, manages project lifecycle, and advises senior management on technical strategy and risk.

Lead multiple AI/ML computational projects across early-stage ventures, define AI strategy, oversee method and pipeline development (including protein design, docking, LLM agents), ensure benchmarking/scaling/productionization, mentor and manage cross-functional teams, scout literature, and represent the organization to partners and conferences.

The role involves developing and integrating ML/AI solutions in agricultural biotechnology, mentoring teams, and promoting data-driven practices.

The Machine Learning Architect will deploy models on prototype hardware, adapt algorithms for new environments, and collaborate with hardware engineers to define architecture based on practical insights.

Lead research on multi-modal reasoning systems, develop training strategies, build datasets, and collaborate with scientists to produce scientific superintelligence.

As a Machine Learning Scientist at Nucs AI, you will develop innovative ML methods for medical image analysis, collaborate with clinicians, and contribute to clinical validation studies, impacting cancer diagnostics.

The Machine Learning Engineer will design and implement ML pipelines and productionize models, collaborating with scientists and engineers on scalable systems.

Architect and manage ML pipelines for training and deployment on cloud platforms, automate CI/CD, and ensure system performance and availability.

The Applied Machine Learning Scientist will develop and optimize machine learning models to analyze TCR-peptide-MHC interactions, collaborating with interdisciplinary teams on therapeutic candidate discovery.

Design, develop, and maintain scalable AI/ML systems and end-to-end pipelines (data preprocessing, model training, prompt engineering). Collaborate cross-functionally, optimize model performance, ensure fairness and security, monitor production systems, troubleshoot issues, mentor junior engineers, and recommend improvements to data infrastructure and tooling.

Lead technical design and execution for an AI/ML mission platform: drive architecture decisions, lead major engineering epics, simplify distributed systems, optimize edge and cloud performance, mentor engineers, integrate ML models and inference pipelines, and represent technical capabilities to DoD stakeholders.

Lead technical roadmap and architecture for ML data infrastructure supporting petabyte-scale pipelines. Manage and mentor engineering team, design ingestion/transformation/serving systems, ensure data quality, lineage, governance, and collaborate with ML and infrastructure teams to deliver production-grade ML data pipelines and feature stores.

Lead and build an ML Acceleration team to define strategy and implement scalable solutions to speed ML training. Hands-on role combining technical design (distributed training, hardware and model optimization) with people management, cross-functional collaboration, impact measurement, and continuous performance improvements for production autonomous vehicle ML systems.

The role involves leading data science and machine learning initiatives, developing predictive models, managing cross-functional teams, and fostering a collaborative environment.

Design and develop large-scale AI systems for healthcare, focusing on ML infrastructure, real-time analytics, and collaboration with teams. Requires extensive experience in software engineering and AI.