Machine Learning Jobs in Cambridge, MA

The Senior AI/ML Engineer designs and implements AI/ML solutions, collaborating with cross-functional teams to enhance product experiences for healthcare professionals.

Lead the development of the ML Data Service platform, manage engineers, drive technical strategy, and ensure high-quality data for ML use cases.

As a Machine Learning Systems Engineer, you will optimize distributed training systems, enhance performance profiling, and develop high-performance GPUs for machine learning workloads.

The role involves training and deploying state-of-the-art ML models, with ownership over research projects and familiarity with data engineering.

Conduct analysis of high-frequency wearable sensor data to develop digital biomarkers using signal processing and AI; build time-series models and data pipelines; integrate multi-modal datasets; collaborate with cross-functional teams and communicate results via reports and publications.

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

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.

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.

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 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.

Develop and implement quantum error correction protocols and decoding algorithms using machine learning methods on unique hardware aimed at fault-tolerant quantum logic execution.

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.

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 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.

The ML Scientist will develop machine learning models for climate risk, collaborate across teams, and validate algorithms to improve predictions of weather extremes.

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.

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.

Lead enterprise AI architecture and platform strategy, designing secure, scalable Generative AI/ML solutions across multi-cloud and hybrid environments. Define reference architectures, governance, observability, and production readiness; run POCs, enable model serving and orchestration, ensure Responsible AI and compliance, and drive architecture adoption with cross-functional teams.

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.

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.

The Data Scientist will develop machine learning models using cancer data to advance oncology understanding and improve patient outcomes, collaborating with multiple teams and publishing results.

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.

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