Top Machine Learning Jobs in Boston, MA

Leads AI and machine learning solution design, deployment, evaluation, and data engineering across client engagements. Establishes portfolio-wide standards for AI quality, robustness, bias, explainability, monitoring, and technical evidence. Advises clients on AI strategy, coaches multidisciplinary teams, sponsors reusable evaluation capabilities, guides business development, and builds executive client relationships while directing multiple workstreams.

Leads payer healthcare data science engagements, developing statistical and machine learning models from claims, member, provider, and utilization data. Directs analytical strategy, exploratory analysis, visualization, feature engineering, validation, and technical problem solving. Advises executive clients on payer modernization, builds business relationships, identifies growth opportunities, mentors teams, and contributes thought leadership in AI, advanced analytics, and healthcare transformation.

Leads payer modernization data science and analytics engineering workstreams for healthcare clients. Manages teams, budgets, timelines, and deliverables while guiding exploratory, statistical, predictive, and machine learning analyses. Oversees data pipelines, models, quality controls, interoperability solutions using FHIR and HL7, and compliance with HIPAA and regulatory requirements. Coaches junior professionals, reviews technical work, collaborates with client leaders, and presents findings supporting platform modernization, cost reduction, and member experience improvements.

Leads client-facing healthcare AI and data science engagements, managing teams that design, develop, validate, and deploy HIPAA-compliant AI and GenAI solutions. Responsibilities include architecture, healthcare process analysis, model and LLM selection, RAG and agentic workflow development, data engineering, MLOps, stakeholder presentations, team coaching, and delivery oversight across clinical, financial, and operational health system use cases.

Lead and develop a team of data scientists and AI/ML engineers delivering scalable, production-grade AI solutions. Drive reusable AI products, platforms, APIs, frameworks, and enterprise architecture. Establish engineering practices including testing, CI/CD, observability, documentation, and MLOps/AIOps. Partner with product, engineering, architecture, and business stakeholders to accelerate AI adoption and transformation across global insurance operations.

Participate in Bose’s six-month co-op program by collecting data, developing machine-learning features for audio products, and automating validation systems to assess toolchain updates and ML model performance. The role requires software engineering study focused on DSP or machine learning for audio processing, Python and MATLAB fluency, and strong communication and collaboration skills. Familiarity with fixed-point DSP and Qualcomm audio chipsets is preferred.

Leads client-facing applied AI engineering engagements in healthcare, from solution design through deployment. Translates clinical and health services needs into AI requirements, architectures, and roadmaps; designs RAG, memory, prompt, and agent orchestration components; guides data, model integration, and cloud implementation; evaluates model performance; advises on AI platforms and governance; and develops LLMOps and AgentOps practices. Coaches teams, resolves delivery challenges, and communicates technical decisions to clients and leaders.

Supports client cloud modernization engagements by designing and building Google Cloud data and generative AI solutions. Develops Python workflows for prompt engineering, retrieval-augmented generation, agent orchestration, and LLM evaluation. Configures Vertex AI, LLM APIs, vector databases, guardrails, and LLMOps processes while applying machine learning and NLP concepts. Collaborates on data platform and analytics modernization, troubleshooting, research, solution design, documentation, and iterative improvement with senior staff.

Leads advanced analytics, machine learning, and AI initiatives for Finance, developing predictive models, forecasting solutions, optimization, automation, anomaly detection, and conversational analytics. Partners with Finance, Data, Technology, and Engineering teams to translate business challenges into scalable solutions, establish governance and testing frameworks, evaluate model performance and bias, and drive adoption. Provides technical leadership and mentorship while supporting Finance transformation and continuous improvement.

Leads strategy, development, deployment, and scaling of AI-powered intelligence products for insurance underwriting. Partners with business leaders, data scientists, engineers, architects, and technology teams to define roadmaps, prioritize investments, deliver machine learning and Generative AI solutions, and drive adoption. Oversees product valuation, governance, monitoring, responsible AI, stakeholder alignment, and measurable business outcomes across underwriting segments and lines of business.

Design, develop, deploy, and optimize foundational AI systems, including LLM inference, foundation model training, agentic workflows, vector search, guardrails, evaluation, and observability. Lead multi-model orchestration, cost-performance governance, architecture reviews, technical roadmaps, and responsible AI standards. Mentor senior technical staff and collaborate with engineers, scientists, product managers, and program managers to deliver scalable AI products.

Build and deploy production-grade AI and machine learning systems supporting biomedical research and drug discovery. Own technical solutions from architecture through deployment, including data pipelines, retrieval systems, knowledge systems, inference services, evaluation frameworks, and human-in-the-loop workflows. Partner with scientists and engineers to transform prototypes into scalable, secure, reliable platforms. Lead technical delivery, make architecture tradeoffs, improve engineering standards, mentor teammates, and promote responsible AI practices.

Research and develop real-time AI algorithms for audio source separation, target sound extraction, and speech enhancement. Train and evaluate PyTorch models using single- and multi-microphone recordings, spatial cues, and auxiliary sensors. Address overlapping speakers, noise, reverberation, motion, perceptual quality, latency, memory, and computational constraints. Collaborate with DSP and embedded software teams to deploy models on physical devices, while contributing to patents, publications, and product demonstrations.

Leads data science strategy and execution for medical and workers’ compensation claims. Builds machine learning, generative AI, agentic workflows, LLM, and RAG solutions to improve adjuster workflows and quality processes. Partners with Claims, Technology, Product, Architecture, UX, and data teams to prioritize opportunities, deliver intelligent systems, establish evaluation and monitoring practices, and measure business impact. Provides technical mentorship and is expected to build a data science team over time.

Six-month research co-op developing and evaluating multimodal sensor fusion, DSP, and machine learning algorithms for room environmental sensing. Responsibilities include collecting microphone, IMU, camera, and light-based sensor data; developing models, training datasets, and evaluation infrastructure; and implementing real-time prototypes. The role requires Python, MATLAB, TensorFlow or PyTorch, signal processing, deep learning, acoustic modeling, and real-time processing knowledge.

Machine Learning/DSP Research Engineer Intern developing and evaluating multimodal sensor fusion, digital signal processing, and machine learning algorithms for room environmental sensing. Responsibilities include collecting microphone, IMU, camera, and light-based sensor data; building machine learning models and training datasets; and developing evaluation infrastructure for real-time applications.

Build and own production Vision AI systems end to end, including model selection, inference pipelines, evaluation, monitoring, and customer-facing features. Shape architecture, collaborate with product, design, and engineering teams, and use AI coding tools to accelerate development. Establish high standards for quality, testing, reliability, security, and privacy while mentoring engineers and taking ambiguous ideas from prototype to production.

Lead AI and autonomous vehicle behavior safety strategy for Level 3–4 automated driving systems. Own behavior safety claims, evidence criteria, validation methodologies, performance indicators, and launch-readiness assurance. Evaluate ML-driven AV performance using simulation, closed-course, and public-road data. Define safety metrics and strategies for imitation and reinforcement learning systems, interpret applicable standards, and influence cross-functional engineering teams and executives.

Develop and lead complex AI and data science solutions, shape AI technical strategy, research emerging technologies, resolve large-scale technical problems, and guide cross-functional teams. The role focuses on scalable data solutions, machine learning, deep learning, large language models, generative AI, NLP, computer vision, Python, and Vertex AI. It requires a bachelor's degree and at least five years of progressive data analytics and data science experience.

Lead teams building Samsara Assistant, Agent Studio, and agent extensibility. Own product growth, activation, answer quality, trust, repeat usage, and proactive agent capabilities. Set technical direction for an AI platform used by customers and internal product teams, partner across Safety and Maintenance, and lead approximately 10 machine learning, applied science, and full-stack engineers through two tech leads. Hire, develop, and manage a high-performing engineering organization.

Leads strategy, roadmap, investment, and execution for horizontal AI capabilities, shared AI platforms, and AI-native cybersecurity products. Translates AI research into reliable, secure, scalable customer solutions; drives adoption across product teams; establishes launch and autonomy criteria; partners with customers and cross-functional leaders on commercialization; and builds a high-performing AI product organization.

Lead measurement and data science strategy for Klaviyo’s AI agents, Composer and Customer Agent. Develop frameworks for adoption, quality, engagement, customer value, cost, and revenue impact. Apply experimentation, causal inference, statistical modeling, conversation analysis, human evaluation, and LLM-based evaluation. Partner with Product, Engineering, Finance, and executives, build governed metrics and scalable analytics, influence product and pricing decisions, and mentor data scientists.

Leads the Safety AI organization and architecture for on-device and cloud-based computer vision and machine learning systems. Oversees sensor fusion, model lifecycle management, platform architecture, experimentation, deployment, monitoring, and continuous improvement across more than two million dashcams. Partners with product, firmware, full-stack, and platform teams to deliver reliable, low-latency safety features. Builds and develops teams of applied scientists and engineers while driving measurable improvements in driver safety and customer outcomes.

Lead the design and implementation of AI initiatives for edge devices. Develop and optimize machine learning solutions using large-scale sensor, video, text, diagnostic, transaction, and location data. Partner with engineering and business teams to prototype AI experiences, ship production systems, adopt research advances, and improve edge model performance. Mentor ML engineers and communicate technical concepts to varied audiences.

Leads a multidisciplinary data science and data engineering team supporting Finance. Sets strategy, prioritizes initiatives, guides analytical models and data architecture, advances AI and machine learning applications, improves data quality and reliability, establishes scalable operating processes, and communicates technical recommendations to Finance and executive stakeholders. The role also manages talent, oversees forecasting and decision-support outcomes, and drives measurable business impact.