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.

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 development of advanced ML models and production-grade systems to optimize pricing, allocation, and auctions in Roku's ad serving platform. Translate business problems into hypotheses, run live experiments, mentor teams, and collaborate cross-functionally to drive marketplace intelligence and demand optimization.

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.

Design and implement GenAI, ML, and NLP solutions for clinical trial operations: fine-tune LLMs, harmonize trial data, build enrollment forecasting and cost-optimization tools, simulate patient enrollment, and communicate technical findings to stakeholders.

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.

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 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.

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.

Design, develop, train, validate, and deploy AI/ML products for life-sciences customers. Advise leadership on technical and commercial feasibility, collaborate with cross-functional and commercial teams, and lead projects in RL, LLM agent harnesses, fine-tuning, governance, and model evaluation.

Lead a small ML engineering pod to turn research into production: design and operationalize training pipelines and low-latency inference services for computer vision, document intelligence, and predictive models. Drive R&D execution, benchmarking, prototyping, migrations from frontier models to fine-tuned/open-source solutions, and mentor early-career engineers while aligning research with product timelines.

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

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.

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

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