Top Hybrid Machine Learning Engineer Jobs in Boston, MA

Leads WHOOP’s cloud-based machine learning organization responsible for sleep, recovery, and training algorithms. Manages ML scientists and engineers, establishes technical standards and development practices, guides architecture and deployment, and partners with platform, hardware, sensor, product, research, and engineering teams. The role requires scaling production ML systems, improving model validation, monitoring, and deployment, and translating physiological sensor capabilities into reliable consumer experiences.

Build and operate production AI systems powering WHOOP Coach, AI Support, and other intelligent product features. Lead AI/ML initiatives across data pipelines, dataset design, LLM fine-tuning, evaluation, deployment, experimentation, observability, and continuous feedback loops. Partner with product and data science to create reliable systems based on member needs and real-world usage. Mentor engineers and data scientists while establishing best practices for applied AI/ML, privacy, security, and production lifecycle management.

Design, train, test, optimize, and deploy machine-learning and deep-learning models using time-series and biosensor data. Develop robust inference systems for constrained edge and cloud environments, monitor algorithm performance and data quality, support regulated health-feature documentation, and conduct applied research. Collaborate with data science, firmware, and engineering teams to translate research prototypes into scalable production features for wearable technology.

Build and lead development of an enterprise AI platform supporting large-scale AI-assisted analysis. Responsibilities include designing, developing, testing, deploying, and supporting foundation model training, LLM inference, similarity search, guardrails, evaluation, experimentation, governance, and observability. The role also requires advancing LLM optimization for scalability, latency, throughput, hardware utilization, and cost while shaping the long-term roadmap for foundational AI systems and mentoring engineering teams.

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.

Develop, optimize, and deploy audio and multimodal machine learning models on embedded and edge devices. Build real-time inference pipelines, convert models to efficient C/C++ implementations, and optimize latency, memory, SRAM, and power across MCUs, DSPs, NPUs, and accelerators. Collaborate with researchers, DSP experts, firmware engineers, and hardware teams to deliver production-ready embedded AI systems using model development, quantization, profiling, and on-device runtimes.

Build and validate ML and statistical models, perform exploratory and descriptive analyses, create visualizations, maintain data pipelines and quality, collaborate with clients to deliver tailored AI/ML solutions, mentor junior staff, and apply modern AI techniques including generative AI and NLP to solve business problems.

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.

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.

Design, implement, and scale end-to-end retrieval-augmented generation (RAG) pipelines. Build and optimize retrieval systems, embeddings, vector search, and LLM orchestration. Collaborate with product, design, and MLOps to productionize robust ML systems across large proprietary datasets.

Designs and operationalizes scalable machine learning platforms, pipelines, infrastructure, and production models. Develops MLOps and LLMOps solutions, including generative AI, prompt engineering, RAG, LLM fine-tuning, and computer vision applications. Collaborates with data scientists, engineers, architects, and cross-functional teams to optimize models and integrate them into enterprise systems. Applies hybrid ML and rule-based systems in regulated insurance environments, mentors colleagues, and supports Agile delivery.

Design, build, and optimize reproducible AI/ML systems and research software across faculty-led projects. Develop accelerator-based training and inference pipelines, optimize kernels and compilers, and improve performance across AWS and HPC environments. Support distributed training, profiling, debugging, deployment, CI, packaging, and reliability. Collaborate with researchers, technical leaders, and AWS partners while documenting technical findings and creating reusable tooling for interdisciplinary research.