Top Hybrid Machine Learning Engineer Jobs in Boston, MA

Own end-to-end matching and ranking systems for out-of-home advertising: design candidate generation and re-ranking models, build and manage data warehouse and pipelines, implement evaluation and monitoring, publish low-latency ranking APIs, and maintain model observability and retraining workflows.

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.

Lead architecture and development of advanced perception systems for Level 4 autonomous vehicles. Build multimodal foundation models, synthetic data and sensor simulation capabilities, knowledge-distillation pipelines, and efficient real-time onboard models. Deploy solutions to autonomous vehicle fleets while mentoring engineers and providing technical leadership. The role focuses on object detection, segmentation, tracking, classification, sensor fusion, closed-loop evaluation, and robust generalization across vehicle generations.

Designs and scales production-grade data and machine learning pipelines for fraud, payment risk, and abuse prevention. Responsibilities include ETL and feature engineering, model training and deployment, data and model monitoring, incident response, code reviews, mentoring, and collaboration with data scientists, engineers, and product managers. The role supports reliable, explainable decision-making across financial and operational systems.

Lead Osmo’s Applied AI organization building olfactory intelligence models for prediction, formulation, reformulation, molecule generation, and deformulation. Hire and develop ML engineers and research scientists, set the model roadmap and evaluation standards, own enterprise accuracy commitments, design data and annotation strategies, and move research models into reliable production APIs. This hands-on leadership role partners closely with chemistry, sensory science, software, product engineering, and perfumers while remaining involved in code reviews and experimentation.

Develop and productionize machine learning models for industrial process optimization using spectral and multimodal sensor data. Responsibilities include designing experiments, building preprocessing and feature extraction pipelines, monitoring model and sensor drift, detecting anomalies, developing ML infrastructure, and applying chemometrics, hybrid modeling, self-supervised learning, probabilistic reasoning, and time-series techniques.

Develops and evaluates machine learning and deep learning models using biomedical imaging, genomic, transcriptomic, clinical, and text datasets. Builds data pipelines and computational tools, performs feature engineering and validation, conducts cancer genomics research, and contributes reproducible code. Collaborates with computational biologists, clinicians, engineers, and external partners to identify biomarkers and generate scientific insights. Communicates findings through reports, presentations, abstracts, and manuscripts.

Designs, builds, and operates production machine learning systems at scale. Develops deep learning models for sequential, multimodal, and spatio-temporal data supporting risk assessment, engagement, and event detection. Partners with research, product, and platform teams to deploy reliable ML products across cloud and edge environments. Responsibilities include software engineering, AWS-based deployment, complex SQL, large-scale data processing, model orchestration, and cross-functional delivery of measurable mobility and road-safety impact.

Own robotic grasping intelligence by designing bespoke neural networks, building cloud training and evaluation pipelines, deploying models to robot fleets, and improving performance through field telemetry. Develop simulation-first manipulation strategies using robotics concepts including motion planning, kinematics, geometry, and perception. Collaborate with perception and motion-planning engineers while setting technical direction for a small applied ML team.

Design, develop, and deploy scalable ML software systems for healthcare. Collaborate with teams to improve products and ensure performance and reliability.