Machine Learning Jobs in Cambridge, MA

Design and develop scalable ML systems for healthcare, collaborating with teams to enhance products and maintain efficient infrastructures.

Lead data-quality and labeling efforts for perception ML: implement scalable quality measures, drive data-centric model improvements, maintain ontology/class structure, evaluate human vs auto-labeling trade-offs, and build metrics for labeling throughput and annotator/vendor quality.

The Principal Machine Learning Engineer will develop optimization algorithms, enhance LLM deployments, and collaborate with research teams on model performance and compression.

As a Machine Learning Developer, you will develop and refine machine learning models for process optimization, working closely with data scientists, designing experiments, and ensuring model reliability.

The Staff Machine Learning Engineer will define and build ML capabilities, design production systems, and lead ML strategy in a high-growth AI cybersecurity company.

Define ML strategy and build production ML systems end-to-end (data pipelines, training, evaluation, inference). Own data strategy, integrate ML with product/backend, write production-quality code, and recruit and mentor the ML team while setting technical direction.

Develop, fine-tune, and deploy LLMs and vision-language models for home security products. Lead research initiatives from ideation to production, collaborate with engineers and product managers, evaluate new multimodal techniques, and integrate capabilities into existing systems to drive business impact.

Develop and iterate machine learning models for complex datasets while collaborating closely with technical teams to shape long-term ML strategy.

Seeking a Machine Learning Engineer with 3 years of experience to develop and deploy ML systems, optimize models, and work collaboratively with teams to enhance ML capabilities.

The Principal Scientist leads AI/ML projects, focusing on pragmatic strategies, cross-functional team coordination, and innovative solutions in computational science.

The Cloud and ML Infrastructure Engineer will maintain complex systems, manage AWS production infrastructure for ML/AI workloads, and design monitoring systems for algorithms.

As an Applied AI/ML Engineer, you'll develop and deploy machine learning models, build data pipelines, and collaborate with scientists to improve materials processing and characterization.

Develop multimodal large language models for autonomous vehicles, optimizing performance and safety through advanced machine learning techniques and real-world data validation.

The role involves developing ML-driven infrastructure for complex physics simulations, enabling autonomous agent behaviors and integrations with large-scale computational systems.

Lead ML/AI-driven computational protein and antibody design efforts, advance de novo and engineering pipelines, collaborate with experimental teams to test and iterate constructs, implement state-of-the-art modeling tools, shape computational strategy, and manage external research partnerships.

The MLOps Engineer focuses on developing tools and processes for ML workflows, empowering engineers, and ensuring high-quality model deployment. Key responsibilities include managing distributed systems, improving observability, and advocating best practices across teams.

Build and deploy LLM-based chatbots and machine learning models, integrate agents with enterprise databases and APIs, perform data preprocessing and model evaluation, automate data pipelines, and collaborate with internal teams and clients.

The Senior Data Engineer defines technical approaches for client projects, evaluates modeling strategies, and conducts internal R&D in applied machine learning and analytics, with a strong emphasis on Python-based solutions and mentorship of junior engineers.

Design, train, deploy, and optimize ML models and pipelines for large-scale, safety-critical systems. Build data ingestion and MLOps infrastructure, validate model reliability, co-author technical reports, and collaborate with engineers and customers to integrate new data sources. Some customer travel required.

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

Contribute to AI Inference projects by designing and developing distributed systems for scalable LLM deployments, focusing on Kubernetes and inference optimization.

As a Senior AI/ML Engineer, you will design and deploy LLM systems, collaborate with cross-functional teams, and manage model deployment in production environments.

As a ML Ops Engineer, you'll develop a centralized data infrastructure, build multi-tenant architecture, manage asynchronous job orchestration, and enhance machine learning pipelines for building upgrades.

As a Senior ML Engineer, you will implement and optimize ML models for production, ensuring performance and scalability while collaborating with data and backend engineers.

Develop and deploy machine learning models for drug discovery, collaborate with chemists and biologists, and enhance model performance with active learning frameworks.