Top Hybrid Machine Learning Engineer Jobs in Boston, MA

Leads the design, development, and operation of enterprise AI and machine learning platforms across public and private clouds. Builds MLOps infrastructure, model training and inference workflows, observability, governance, automation, and deployment capabilities. Provides technical leadership, resolves complex platform issues, influences architecture, partners with cross-functional teams, and mentors engineers. The role emphasizes scalable, secure, reliable AI operations, generative AI enablement, infrastructure as code, CI/CD, monitoring, and enterprise platform adoption.

Design, build, deploy, scale, and maintain machine learning models and platforms in production. Responsibilities include developing ML infrastructure, data pipelines, cloud architectures, distributed systems, model monitoring, retraining, testing, CI/CD, and responsible AI practices. The role collaborates with Product, Data Science, and Agile engineering teams while applying advanced modeling techniques and managing large-scale containerized ML systems.

Design, develop, and operate scalable VoIP infrastructure supporting AI-driven voice workloads. Integrate telephony systems, LLM applications, and real-time communication platforms while improving observability, reliability, deployment, and SRE practices. Build tested, maintainable software across telephony and AI layers, support production environments, collaborate with engineering and product teams, and mentor colleagues.

Develops, tests, evaluates, and deploys machine learning models using established architectures and frameworks. Supports feature engineering, data preprocessing, automated training and monitoring pipelines, and production AI integration. Collaborates with engineering and product teams on AI requirements, researches methods to improve model performance, and supports urgent IT incidents. The role requires strong Python and machine learning experience, knowledge of deep learning and generative AI, and advanced quantitative education.

Build and scale core ML platform infrastructure for model training, deployment, feature storage, real-time retrieval, inference, and monitoring. Lead architecture and technical execution, write production backend code, conduct design and code reviews, mentor engineers, improve reliability and cost efficiency, and partner with ML, product, data science, infrastructure, and security teams on platform strategy and roadmap.

Leads the technical direction, architecture, and roadmap for agentic machine learning systems processing global trade data. Builds ingestion, document understanding, entity resolution, inference, evaluation, observability, and feedback systems. Adapts and post-trains LLMs, ships production AI agents, mentors engineers, sets technical standards, and partners with product, engineering, data science, and customers to deliver risk, compliance, and enforcement capabilities.

Develop and optimize deep learning algorithms for resource-constrained embedded audio devices. Build software prototypes, deploy models, research efficient techniques such as quantization and pruning, evaluate models, and assess neural-network hardware accelerators. The role focuses on audio ML for headphones, earbuds, speakers, soundbars, and wearables, requiring embedded C/C++, Python, deep learning frameworks, and collaboration across teams.

Develop and deploy signal-processing algorithms and machine-learning models that convert wearable sensor data into real-time physiological insights. Responsibilities include data analysis, model training, Python prototyping, optimized C/C++ implementation, embedded firmware integration, and production validation. The role requires collaboration with firmware, hardware, data science, and domain teams to optimize accuracy, power, memory, compute, and latency while conducting rigorous offline, laboratory, real-world, and on-device testing.

Lead design and delivery of advanced data and ML solutions, performing exploratory analysis, statistical modeling, and visualization to inform strategy. Manage and coach teams, collaborate with clients to validate outcomes, and improve analytics processes and data quality to drive business decisions.

Build and maintain internal agentic AI platforms and developer-facing tools that integrate LLMs with engineering workflows. Partner with product and infra teams to design standards for agent deployment, reliability, evals, security, onboarding, and adoption measurement to improve developer productivity.