Top Hybrid Machine Learning Engineer Jobs in Boston, MA

Design, implement, and deploy scalable AI/ML systems. Perform data wrangling, preprocessing, and advanced algorithm development. Collaborate with cross-functional teams to integrate AI solutions, analyze complex datasets for business insights, and support client engagements while continuously improving technical skills.

Lead ML Engineer for Finance Tech to design, build, deploy, and maintain AI/ML systems (including LLMs and RAG). Partner cross-functionally to create production data pipelines, model evaluation, governance, observability, and responsible AI practices while contributing technical vision and scaling AI infrastructure.

Lead end-to-end machine learning projects to improve customer engagement and retention. Build, test, and deploy models for user forecasting, personalization, and Casino engagement. Partner cross-functionally to scale solutions, mentor junior data scientists, and communicate model impact to influence product and marketing strategy.

Lead program management for Core Algorithm Development, coordinating algorithm, data science, firmware, software, product, and validation teams to plan, validate, integrate, and release physiological algorithms while improving processes, mitigating risks, and reporting progress to executives.

Lead development of predictive and prescriptive supply chain models using statistics, machine learning, and optimization. Partner with IT, product managers, and engineers to scale analytics products, design experiments, run simulations, mentor analysts, and translate insights into operational improvements.

Individual contributor on an applied science team building production ML and software systems for media accessibility. Responsibilities include prototyping, evaluation, productionization, experiment design and analysis, code and design reviews, and reporting results for audio/video/NLP/generative AI projects.

The Machine Learning Scientist will develop AI frameworks for predictive modeling in oncology, collaborating with teams to enhance drug R&D efforts through advanced machine learning techniques and multimodal data integration.

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

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.

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.