Top Machine Learning Engineer Jobs in Boston, MA

As an ML Engineer Co-op, build prototypes and deep learning algorithms for audio tasks, collaborate with teams, and showcase innovations.

The role involves designing and operating AI systems, building data pipelines, fine-tuning models, and mentoring team members while collaborating with product and data science teams.

The role involves deploying ML models for ads, collaborating with teams to enhance solutions, and mentoring junior scientists, requiring advanced technical and communication skills.

Design and deploy production-grade ML systems, integrate models into software pipelines, and develop features leveraging ML for optimization and prediction problems.

The Machine Learning Engineer will design, implement, and manage complex ML algorithms, focusing on computer vision and data analysis while collaborating with team members to transition algorithms to production.

The role involves building computer vision models, researching deep learning methods, handling large datasets, and implementing MLOps best practices.

The Principal ML Engineer at Agero will develop and productionize a Dispatch System using ML models, optimize decision-making processes, and lead a team to improve operational performance.

Design, develop, and deliver machine learning models for clinical data. Collaborate with stakeholders and optimize production ML systems.

As a Staff Machine Learning Engineer, you will develop and maintain ML models for decision-making, optimize pipelines, and collaborate with a diverse engineering team.

As a Machine Learning Engineer, you will perform analysis on healthcare data, develop and deploy clinical machine learning models, and collaborate with various stakeholders.

The role involves designing and optimizing large-scale multimodal AI models, conducting applied research, and collaborating with cross-functional teams to deliver impactful member value at WHOOP.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

The Senior Applied Scientist II is responsible for designing and implementing ML models to optimize the supply chain, collaborating across teams, and driving data-driven decision-making.

Lead machine learning initiatives to enhance underwriting model accuracy. Design and implement advanced training strategies and optimize workflows. Mentor engineers and influence modeling strategy.

The Machine Learning Engineer will design, build, and maintain production machine learning systems, focusing on scalability, reliability, and integration with existing products. Responsibilities include developing ML pipelines, establishing ML Ops best practices, and collaborating on product capabilities.

Lead design and implementation of enterprise-grade AI systems, architect AI infrastructure, and drive technical strategy for AI-powered products primarily for military applications.

Develop and optimize machine learning models for data extraction from tax documents, collaborate on software integration, and mentor junior team members.

As a Senior Staff Machine Learning Engineer at Affirm, you will lead complex ML system design and implementation, mentor engineers, and drive innovative ML strategies for financial services.

Design, build, and productionize scalable ML systems for physiological time-series data. Collaborate with data scientists, engineers, and product teams to deploy reliable, low-latency inference services, improve ML pipelines, and maintain production uptime.

Partner with CV researchers to productionize models, optimize training and real-time inference performance, design reusable abstractions, and collaborate with MLOps/backend teams to evolve the CV inference platform for scale and reliability.

Design, build, and scale production ML systems (including LLMs) to automate customer support and compliance workflows. Lead technical roadmaps, write high-quality Python code, mentor engineers, ensure secure, scalable architectures, and resolve complex reliability issues.

The co-op involves developing DSP and ML algorithms focused on room acoustics, conducting acoustic simulations, and researching audio signal processing methods.