Top AI Jobs in Boston, MA

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 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.

Research and develop real-time AI algorithms for audio source separation, target sound extraction, and speech enhancement. Work with single- and multi-microphone recordings, spatial cues, auxiliary sensors, and challenging acoustic conditions. Train and evaluate PyTorch models while assessing perceptual quality, latency, memory, and computational efficiency. Collaborate with DSP and embedded software teams to deploy models on physical devices and contribute to patents and research publications.

Build and deploy generative AI solutions, including APIs, MCP servers, AI agents, LLM integrations, and full-stack applications. Develop scalable AWS cloud services, observability systems, evaluation pipelines, and production agent workflows. Collaborate with Product, UX, Security, Legal, and engineering teams to design interfaces, adapt AI tools to company codebases, and support customer experience and developer productivity use cases.

Lead a new AI-focused data science team accelerating insurance pricing program rollouts. Partner with pricing, delivery, and technology teams to identify AI opportunities; build evaluations for AI and agentic systems; define metrics, statistical tests, ground truth, and production experiments; measure value and risk; translate business needs into requirements; and prototype evaluation tools and AI applications. The role requires insurance pricing expertise, advanced statistics, strong Python and data science skills, modern LLM experience, and effective stakeholder communication.

Research, design, train, and deploy multimodal AI models using clinical and molecular data to support precision medicine, biomarker discovery, therapy outcome prediction, and companion diagnostics. The role requires collaboration with life sciences companies, regulatory alignment for healthcare products, and publication of original research. Responsibilities include working with pathology slides, genomic profiles, spatial omics, and longitudinal patient records while contributing hands-on model development and product-level engineering.

Build and deploy foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for latency, cost, throughput, scalability, and hardware utilization. Own complex architectures, reliability SLOs, security and compliance reviews, technical roadmaps, and cross-functional delivery while mentoring engineering teams.

Lead AI and data science efforts that accelerate insurance pricing program rollouts. Partner with pricing, delivery, and technology teams to identify AI opportunities, build evaluations for AI and agentic systems, define metrics and statistical tests, measure value and risk, and develop evaluation tooling and AI applications. Apply insurance pricing expertise, modern LLM experience, experimental design, and strong stakeholder communication to move high-value solutions into production.

Leads health economics and outcomes research and real-world evidence projects for pharmaceutical, biotech, academic, and other partners. Designs and analyzes pharmacoepidemiologic studies, treatment patterns, healthcare utilization, causal inference, and survival analyses. Builds reusable analytical infrastructure, collaborates with scientists, engineers, and product teams, interprets results for stakeholders, supports publications and regulatory work, and ensures compliance with data governance and applicable regulations.

Leads the design and implementation of AI-powered Finance workflows, agents, copilots, and integrations across ERP, billing, procurement, and related systems. Establishes AI architecture, governance, security, testing, observability, and responsible-use practices. Partners with Finance, IT, Security, Legal, and Audit to identify automation opportunities, define success metrics, build reusable frameworks, and enable nontechnical teams. Requires technical leadership, enterprise financial systems integration, LLM experience, and the ability to build scalable 0-to-1 solutions.

Conduct fundamental research on on-device LLM efficiency and optimization. Develop proposals, survey academic literature, formulate hypotheses, and run experiments involving quantization, fine-tuning, compression, decoding, and model architectures. Collaborate with internal teams and academic labs, transition prototypes into production, publish at leading AI conferences, and independently drive research initiatives from ideation through implementation.

Develop, train, evaluate, optimize, and debug machine learning models for consumer audio applications. Focus on efficient AI and Edge ML by reducing latency, memory usage, and computational demands. Collaborate with software, embedded systems, and hardware engineers to deploy models on resource-constrained devices. Explore techniques such as quantization, pruning, and model compression while contributing to emerging AI technologies and practical product development.

Leads data science for enterprise agentic AI products, developing and deploying machine learning models, designing experiments, analyzing telemetry, and translating findings into business strategy. Partners with data scientists, AI/ML engineers, and product managers while guiding large-scale analysis across numeric and textual data. The role also requires technical leadership, stakeholder communication, innovation, talent development, and expertise in statistical modeling, deep learning, and cloud-based data science.

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.

Develop, test, deploy, and serve machine learning models and agentic AI capabilities. Build feature engineering, experimentation, training, monitoring, and REST-based model-serving workflows. Collaborate with data science, engineering, product, and MLOps teams to productionize AI solutions at enterprise scale using distributed computing, Databricks, cloud platforms, and containerized infrastructure. Support model performance improvements, generative AI initiatives, and urgent IT escalations.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Own complex architectures, define reliability objectives, lead technical reviews, ensure security and compliance, shape long-term AI roadmaps, and mentor engineering staff.

Leads AI transformation, governance, quality oversight, risk management, and responsible adoption across Pfizer Global Supply and QSEO. Establishes GxP-compliant AI lifecycle practices, inspection-readiness frameworks, governance forums, portfolio monitoring, inventory management, metrics, and reporting. Advises senior leaders on AI regulations, deployment risks, investment priorities, and compliance while leading cross-functional initiatives and influencing stakeholders across Quality, Digital, Operations, and R&D.

Design, develop, optimize, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, orchestration, governance, and observability. Lead cost-performance optimization across production AI platforms, establish engineering standards, contribute to technical roadmaps, lead design reviews, and mentor senior AI engineers. Collaborate with research, engineering, program management, and product teams to deliver scalable, responsible AI products.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI infrastructure for cost, latency, throughput, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term AI platform strategy, and mentor engineering colleagues.

Design and implement AI agents, plugins, automations, and enterprise integrations for HR Technology platforms. Lead requirements gathering, technical analysis, solution design, testing, documentation, deployment, security, and data integrity efforts across HR, IT, and business stakeholders. Integrate platforms including ServiceNow, Workday, and other enterprise applications while supporting the HR automation and AI roadmap.

Build developer tooling, reusable frameworks, AI agent workflows, MCP integrations, and cloud-native services that improve engineering productivity. Evaluate and roll out AI development technologies, establish standards and guardrails, support authentication platform evolution, and improve AWS service and job creation paths. The role also involves documentation, collaboration with engineering and product teams, and improving system reliability and performance.

Leads Samsara’s GTM AI Engineering team, building production AI agents, copilots, workflow automations, predictive models, and evaluation systems. Owns the shared AI platform, architecture, governance, observability, model routing, cost controls, and CRM intelligence. Partners with Sales, Customer Success, Support, Product, and Engineering to drive adoption and measurable business impact. This hands-on director role involves coding and prototyping approximately 70% of the time while hiring, developing, and guiding the engineering team.

The Principal AI/ML Engineer will lead the development of AI/ML platforms, drive technology architecture, ensure quality and integration, and provide strategic leadership while managing a team of engineers.

Leads OuterBox’s internal AI strategy, AI engineering team, and development of client-facing AI services. Identifies market opportunities, converts internal capabilities into priced consulting offerings, oversees production deployments and partner programs, and drives AI adoption, training, and measurement. Partners with executives, clients, engineering, and technology teams on architecture, security, cloud governance, reliability, pricing, and ROI.

Leads enterprise AI platform adoption by embedding with IT teams, building agentic workflows and integrations, improving software delivery and operations, and measuring engineering efficiency. Applies AI across the SDLC, DevSecOps, observability, security, governance, and legacy modernization. Translates field learnings into reusable platform capabilities, influences senior stakeholders, mentors engineers, and serves as a technical escalation point without direct management responsibility.