Top Hybrid Machine Learning Engineer Jobs in Boston, MA

Develop and deploy production machine learning models and infrastructure at scale. Responsibilities include model development, data pipeline construction, cloud and Kubernetes operations, distributed data processing, CI/CD automation, model monitoring and retraining, responsible AI governance, and collaboration with product and data science teams. The role requires strong programming, machine learning, distributed systems, and production deployment expertise.

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.

Design, train, optimize, and deploy efficient machine learning models for consumer audio products and resource-constrained edge devices. Apply quantization, pruning, distillation, and model compression while balancing accuracy, latency, memory, compute, and power requirements. Collaborate with embedded, DSP, hardware, compiler, and software teams on deployment and integration, troubleshoot model issues, develop repeatable ML workflows, evaluate emerging technologies, document findings, and mentor interns and co-op engineers.

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 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.

Provide technical leadership for CarGurus’ recommendation platform, defining architecture and building scalable personalized ranking and discovery systems. Partner with Data Science to productionize machine learning models, improve experimentation and recommendation quality, and deliver AI-powered consumer experiences. Lead complex cross-functional initiatives, improve reliability and observability, make organization-wide technical decisions, write code, mentor engineers, and uphold quality, security, and operational standards.

Perform Bayesian and probabilistic modeling, test and evaluate ML/AI systems, analyze complex datasets, develop algorithms, and transition solutions into software and real-time embedded systems for national security customers. Communicate results and propose technical paths forward.