Machine Learning Jobs in Cambridge, MA

Build and productionize agentic AI systems that retrieve and synthesize structured and unstructured financial data. Work across the ML lifecycle: experiment, deploy, monitor, and evaluate agents and retrieval methods. Collaborate with data, product, design, and MLOps teams to create scalable, robust solutions that connect data to business insights.

Lead on-device ML inference deployment and performance optimization for outdoor camera/doorbell workloads. Optimize latency, throughput, memory, power and stability across CPU/DSP/NPU/GPU. Perform kernel/operator-level tuning, runtime integration in C/C++, quantization validation, and build profiling/benchmarking tooling. Drive cross-functional work with ML, firmware, and hardware teams and provide staff-level technical leadership and mentoring.

Lead strategy, development, and production deployment of scalable AI/GenAI and ML solutions. Drive technical direction, mentor data scientists, own end-to-end initiatives, ensure MLOps and model governance, collaborate cross-functionally, and deliver measurable business impact using advanced models (LLMs, transformers, diffusion) and cloud platforms.

The Senior Machine Learning Engineer will develop, deploy, and optimize large language models on healthcare data, collaborate with teams, and ensure compliance.

Work with the machine learning team and portfolio managers to translate investment questions into ML problems, develop and evaluate data-driven models (including simulation and optimization), maintain production models and tools, document methods and results, and stay current with ML/AI techniques relevant to finance. Gradually take ownership of features, components, and end-to-end projects.

Lead R&D on LLM training and inference optimization, train and evaluate large-scale models on Tenstorrent hardware, improve performance via techniques like speculative decoding, quantization, kernel fusion, flash attention and distributed training, diagnose system bottlenecks, and translate research into scalable production solutions through cross-functional collaboration.

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.

Develop and train computer vision and visual representation pipelines for robotic manipulation (RGB, RGB-D, depth, segmentation, pose, keypoints). Build perception models to support RL and imitation policies, improve synthetic/real data pipelines, evaluate models on real robots, calibrate sensors, and collaborate with robotics and simulation engineers to iterate on models and data for robust deployment.

Develop and implement reinforcement and supervised learning algorithms for robotic manipulation, build simulations and domain randomization, optimize model architectures and data pipelines for sample efficiency, and deploy, evaluate, and debug policies on physical robots.

Design, implement, and maintain large-scale ML/RL training infrastructure: job orchestration, scheduling, checkpointing, experiment tracking, developer tooling, distributed training, and resource management for cloud compute.

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.

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.

Build and operate model validation, quantization, and safety systems for production ML. Develop pipelines for security scanning, optimization (quantization/pruning), routing, prompt management, and evaluation frameworks measuring accuracy, performance, and safety across model lifecycles.

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.

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 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 and deliver AI/ML and computational projects that accelerate pharmaceutical R&D across preclinical-to-clinical stages. Build, scale, benchmark, and maintain agentic systems combining ML and computational biology tools; manage cross-functional teams; drive strategy, resourcing, best practices, and stakeholder communication across ventures and platform partners.

Lead a team building large-scale ML model evaluation and orchestration frameworks for autonomous vehicles. Own data analysis workflows, dataset creation, statistical model evaluation, regression reporting, and tool development (AWS Kubernetes orchestration, ReSimulation). Provide technical leadership, mentorship, and manage project timelines and deliverables.

Lead product strategy and execution for DriveWell Fusion, focusing on telematics data cleansing, signal processing, and AI/ML model development. Collaborate with engineering, customers, and cross-functional teams to integrate map data, deliver actionable driving insights, and use Databricks/Tableau analytics to inform roadmap and adoption. Serve as primary product contact, prioritize features, and drive customer-facing advisory work to improve safety and engagement.

Turn research prototypes into scalable, fault-tolerant ML and physics infrastructure for drug discovery. Improve GPU utilization, distributed execution, reproducibility, orchestration, monitoring, and packaging so scientists can run reliable, multi-cluster workflows and agent-usable pipelines.

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

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

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