Top Remote Machine Learning Jobs in Boston, MA

The role involves building predictive models for blockchain performance and user behavior, implementing solutions, and owning production prediction pipelines.

As a Machine Learning Scientist at Nucs AI, you will develop innovative ML methods for medical image analysis, collaborate with clinicians, and contribute to clinical validation studies, impacting cancer diagnostics.

Lead and grow a team of ML, backend, and data engineers to design, deploy, and operate production-scale personalization and recommendation systems. Set technical direction, partner with Product and Data Science, mentor engineers, prioritize delivery, and champion adoption of modern ML techniques including large language models and generative AI to improve user personalization.

As a Software Engineering Intern, you will contribute to building features for user-facing products, APIs, and scalable services for machine learning. Responsibilities include crafting code, designing data pipelines, implementing tools, and enhancing technical documentation.

Lead and manage the engineering team to design, build, deploy, and maintain cloud-based social media/influencer marketing and AI platforms. Oversee software architecture, large-scale data pipelines, image/video processing, and AI/ML solutions. Coordinate cross-functional work, hiring, project planning, Agile delivery, and continuous system improvement.

The Machine Learning Engineer will work on unstructured data classification and clustering, utilizing their expertise in AI systems and various programming languages.

The Senior Machine Learning Engineer will utilize deep experience and insights to enhance communication and digital interactions through advanced machine learning tools and algorithms, focusing on unstructured data.

Build, evaluate, and deploy LLM/ML models and synthetic datasets to improve Community Support. Identify opportunities via data exploration, analyze structured and unstructured data, automate LLM evaluation, personalize experiences, and collaborate cross-functionally to drive product and operational impact.

The Staff Machine Learning Engineer will lead ML systems development for personalization and ranking, design architectures, and mentor team members, ensuring reliable operations at scale.

The Junior-Midlevel Developer will build and enhance Dumroo.ai's AI-powered education platform, collaborating on software components, data pipelines, and machine learning features.

Own and scale delivery of complex AI data programs (LLM/ML products), ensuring scope, quality, timeline, and margin targets. Translate ambiguous problems into technical workstreams, establish governance, drive solution reviews, embed responsible AI practices, manage vendors, and lead cross-functional teams while partnering with BD and executive stakeholders.

Design, build, and deploy novel sensor hardware and data acquisition systems; curate geoscientific datasets; develop statistical, physics-based, and ML models (2D/3D) to predict mineralization; create visualizations and evaluation tools; collaborate with geoscientists, engineers, and vendors; deploy sensors in the field and integrate results to guide exploration programs.

Lead machine learning initiatives, oversee product development, improve execution pace, enhance team reliability, and foster team growth at Hightouch.

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.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Lead the development and implementation of machine learning solutions, managing a team to drive strategic decisions and innovative business outcomes.

Design, train, and deploy scalable ML models (generative, classification, regression). Define feature roadmaps, collaborate with engineering to implement code and APIs, adapt ML methods for distributed/GPU environments, and create evaluation/annotation programs for fine-tuning and performance measurement.

Design and productionize ML models and agentic systems for intraday financial alpha generation. Build data pipelines, experiment frameworks, and portfolio optimization methods. Collaborate with quants and engineers to move models from research to live trading environments.

Develop and tune ML models, productionize and deploy strategies for ML systems, and align cross-functionally to communicate performance metrics.

As a Senior Machine Learning Engineer, you will enhance diffusion models, conduct research, collaborate on system implementations, and communicate findings effectively.

Develop, validate, and improve machine learning models (regression, classification, forecasting, optimization). Prepare data, perform feature engineering, train and evaluate models, and support deployment with Data/ML Engineering. Communicate results, document methods, and collaborate on experiments and cross-functional initiatives.

Lead development of internal Python SDKs and agentic AI workflows, build scalable FastAPI ML services, maintain CI/CD with Jenkins/GitLab Runners, and use Galileo for model evaluation and observability to productionize autonomous systems.

Lead end-to-end development of fraud and risk ML systems: define problems, engineer features, build and evaluate models, collaborate with product and engineering, deploy with ML engineers, and monitor production performance to detect drift and trigger retraining.

Lead and deliver high-value AI/ML consulting engagements: design, build, and deploy ML models and ETL pipelines; analyze large disparate datasets; create visualizations and executive presentations; develop business and client relationships while managing project teams and driving measurable client outcomes.

Lead AI/ML consulting engagements: design and implement ETL, data modeling, predictive models, anomaly detection, distributed data pipelines, and executive-ready visualizations. Drive business development, mentor teams, and deliver client-facing solutions in hybrid/onsite environments.