Machine Learning Jobs in Cambridge, MA

Design, build, and maintain MLOps software systems and ML pipelines; deploy, test, and monitor AI/ML models on cloud-native platforms; implement ETL and data workflows; ensure model performance, security, and scalability; lead technical roadmap, mentor engineers, and evaluate emerging MLOps tools.

The Director of Product will lead product strategy, manage product execution and drive innovation in drug discovery and scientific tools, ensuring alignment between customer needs and the scientific team's goals.

As a Solution Architect, you will design and architect modern IT systems for healthcare, collaborating with product owners and leading technical discussions to improve efficiency and service delivery.

Lead the Oncology Data Science team in developing data-driven solutions for low molecular weight drug development, leveraging AI and machine learning. Collaborate with stakeholders to execute data science strategies and ensure adherence to best practices in analytics.

Develop and optimize medical large language models, collaborate with interdisciplinary teams, and maintain state-of-the-art machine learning techniques to enhance medical education and clinical decision support.

Lead design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, evaluation, and inference architecture. Collaborate with data and infra teams, drive reproducible model development, and provide technical leadership and mentorship from research through production.

Design, build, and deploy production ML systems that combine structured clinical/EHR data with computer vision outputs. Own model calibration, interpretability, monitoring, drift detection, retraining workflows, and regulatory documentation. Partner cross-functionally, mentor engineers, and translate clinical questions into reliable, production-grade ML solutions.

Lead finance analytics strategy owning data warehouse, BI, GCP cost optimization, and Finance AI automation. Build a reporting Center of Excellence, establish data pipelines and AI model foundations, enable self-service analytics, ensure data governance and audit readiness, and manage a team of engineers and a finance/strategy manager.

Develop and apply advanced machine learning models that link chemoproteomics, quantum chemistry, and electronic structure to drive generative molecular design for covalent drug discovery. Build featurization and modeling pipelines, perform data curation and QC, design rigorous benchmarks, partner with medicinal chemistry and engineering teams to productionize workflows, and communicate results across cross-functional teams to support compound design and experimental iteration.

Design and deploy production recommendation and retrieval engines, build data orchestration pipelines for multiple modalities, implement agentic workflow frameworks, and prototype, optimize, and scale models with monitoring, governance, and low-latency SLAs.

Develop and apply machine learning methods linking quantum chemistry, DFT-derived descriptors, and chemoproteomics to drive covalent drug discovery. Build and benchmark generative and molecular ML models, curate and process large molecular datasets, and partner with chemistry, biology, and engineering teams to productionize workflows and support compound design and prioritization.

The role involves applying machine learning in drug discovery, leading model development, collaborating with cross-functional teams, and mentoring staff.

Lead development of multi-modal 2D/3D perception systems for warehouse robots, own dataset and data pipeline, deploy optimized models to NVIDIA Orin edge hardware, write production-grade Python/C++ code, mentor engineers, and drive model robustness via active learning and QA.

The Senior Machine Learning Scientist will lead R&D in AI applications, focusing on LLM, CV, and GenAI technologies, collaborating with teams to develop and optimize innovative models and algorithms.

The Staff Software Engineer will lead the development of cloud-side ML infrastructure, focusing on real-time computer vision and LLM applications, while mentoring team members and improving operational reliability.

The Staff ML Engineer will lead architecture decisions for ML infrastructure, operate real-time computer vision systems, and mentor engineers, ensuring high-quality ML deployment and operational excellence at SimpliSafe.

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

Lead architecture and technical strategy for Spotify's agent-powered features, partnering with cross-functional teams to build, evaluate, and scale LLM-driven systems. Provide servant leadership to multiple teams, manage ~5-10 people, influence product roadmaps, and drive consolidation of agent implementations onto a single scalable engine.

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.

Refactor, debug, and improve Python code for clarity, robustness, performance, and maintainability. Design modular, reusable components, implement basic testing and validation, translate research/experimental code into production-quality modules, and collaborate with engineers using standard software engineering practices.

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

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.

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.

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.