Top AI Jobs in Boston, MA

Conduct applied research in AI safety and multimodal systems, developing foundation models across design, training, evaluation, validation, and deployment. Collaborate with data scientists, engineers, and product managers to deliver AI products at scale. Research areas include large language models, optimization, self-supervised learning, robustness, explainability, red teaming, safety guardrails, and multimodal vulnerability assessment. Publish findings, reproduce research, and independently pursue impactful research agendas.

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize models and infrastructure for cost, latency, throughput, and hardware utilization. Architect multi-model orchestration pipelines, establish AI engineering standards, lead technical reviews, and mentor senior AI engineers.

Conduct applied AI research and build foundation models through design, training, evaluation, validation, and deployment. Lead research initiatives spanning large-scale deep learning, language models, optimization, fine-tuning, and production inference. Collaborate with data scientists, software and ML engineers, product managers, and business leaders to translate research into scalable AI products and customer experiences.

Leads an AI engineering team developing scalable generative AI platform services, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability, and agentic workflows. Sets technical vision, makes build-versus-buy decisions, optimizes production AI systems, operationalizes Responsible AI standards, develops team roadmaps, collaborates cross-functionally, and mentors engineering talent.

Design and build generative AI proofs of concept, pilots, reusable components, APIs, and production-ready reference implementations for commercial insurance. Apply RAG, AI agents, intelligent document processing, model integration, and structured outputs. Evaluate solutions for quality, safety, reliability, and business value while establishing secure, observable, scalable architectures. Partner with technical and business stakeholders, mentor engineers, communicate recommendations to leadership, and shape technical direction across initiatives.

Build and scale infrastructure for AI research across world modeling, forecasting, user modeling, and optimization. Prototype and deploy AI systems in production, partner with customers and stakeholders, and contribute reusable capabilities to the Mosaic platform. The role requires distributed systems expertise, ownership in ambiguous environments, strong communication, and the ability to translate research concepts into reliable, deployed products.

Research and develop real-time AI audio understanding algorithms for physical devices. Build models for sound event detection, localization, acoustic scene analysis, anomaly detection, and multimodal context understanding. Create datasets, pipelines, evaluation methods, and demonstrations while optimizing accuracy, latency, and compute cost. Collaborate with DSP and software engineers to integrate models into product platforms and contribute to patents or conference publications.

Lead the architecture and delivery of an agent-native app-building product while managing and mentoring a team of 3–5 engineers. Establish engineering standards for testing, observability, and AI-driven development; partner with infrastructure and product teams; and build scalable SaaS and agentic AI systems involving orchestration, context management, retrieval, vector stores, tool invocation, and evaluations.

Conduct interdisciplinary AI and human systems research with Microsoft Research mentors and fellow doctoral interns. Contribute to studies involving AI evaluation, human-AI interaction, societal impacts, agentic systems, markets, and institutions. Collaborate with researchers, present findings, develop original research, and contribute to the research community during a 12-week internship.

Lead end-to-end performance of customer-facing conversational AI and knowledge base. Design, test, and optimize AI conversations, define intents and success metrics, analyze KPIs, maintain enterprise knowledge, run continuous improvement using analytics and experimentation, and partner cross-functionally to improve CX and business outcomes.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

Conduct applied AI research and build foundation models, including vision-language models, across design, training, evaluation, validation, and deployment. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products. Develop research methods, reproduce and extend published work, optimize training and inference, and contribute to technical reports and conference submissions. Own research problems and agendas while translating complex findings into business outcomes.

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.

Develop AI-enabled software solutions for assurance platforms and client applications. Design, code, test, troubleshoot, and maintain scalable Python and API-based applications using cloud, DevOps, CI/CD, LLM, prompt engineering, and RAG technologies. Translate business requirements into technical designs, document delivery decisions, collaborate with clients and teams, review code, resolve issues, and guide junior developers.

Conduct bioinformatics and data science analyses to identify and prioritize vaccine targets. Develop comparative genomics, phylogeny, functional prediction, and omics analysis methods; build pipelines, databases, and dashboards; interpret findings; and communicate results to computational and experimental teams. Coordinate cross-functional Vaccines R&D projects and contribute to scientific publications, reports, and presentations.

Leads the design, development, and operation of enterprise AI and machine learning platforms across public and private clouds. Builds MLOps infrastructure, model training and inference workflows, observability, governance, automation, and deployment capabilities. Provides technical leadership, resolves complex platform issues, influences architecture, partners with cross-functional teams, and mentors engineers. The role emphasizes scalable, secure, reliable AI operations, generative AI enablement, infrastructure as code, CI/CD, monitoring, and enterprise platform adoption.

Owns the vision, roadmap, governance, and lifecycle management of AI/ML platform capabilities. Translates stakeholder needs into platform requirements, prioritizes integrations and improvements, partners with engineering teams, drives adoption, and monitors reliability, performance, security, cost, and technical debt. The role also supports agile delivery, communicates releases and incidents, reports progress to leadership, and assists with critical IT escalations.

Build and deploy scalable, responsible AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architectures, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to platform roadmaps, and mentor engineering staff.

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

Develop and validate clinical heuristics and algorithms using real-world healthcare data. Build Python profiling pipelines, automated data-quality checks, and longitudinal patient cohorts. Analyze complex clinical databases and ETL pipelines, evaluate AI, NLP, and heuristic outputs, and collaborate with clinical, labeling, engineering, and data science teams. Maintain documentation, configuration, metrics, and validation standards for transparent, high-quality clinical analytics.

Develops and oversees enterprise AI governance policies, standards, controls, monitoring, and risk-management processes. Advises business and technology leaders on responsible AI, regulatory requirements, controls, and mitigation strategies. Coordinates with cybersecurity, privacy, legal, compliance, architecture, and engineering teams; evaluates control effectiveness; drives corrective actions; supports audits, executive reporting, and governance committees; and promotes adoption through stakeholder education. Requires 8+ years in data analysis, reporting, or related fields, with SQL, analytics, data modeling, and visualization experience.

Develop AI and data analytics solutions for AML and sanctions clients within PwC’s Risk & Regulatory practice. Responsibilities include Python-based data analysis, designing data architectures and pipelines, creating dashboards, interpreting complex datasets, engaging stakeholders, and delivering actionable insights and tailored client solutions. The role also involves client relationship building, problem-solving, continuous improvement, and contributing to strategic business outcomes.

Leads AI and data science initiatives that accelerate insurance pricing program rollouts. Builds evaluations for AI and agentic systems, defining accuracy metrics, ground truth, statistical tests, edge cases, and production experiments. Measures business value and risk, prototypes evaluation tooling and AI applications, and partners with pricing, technology, delivery, and business teams. Requires insurance pricing model experience, advanced statistics, strong Python coding, modern LLM experience, and the ability to communicate complex findings to stakeholders.

Leads enterprise AI engineering strategy and multi-team execution. Oversees foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability, and production optimization. Makes technology decisions, scales reusable AI infrastructure and responsible AI practices, partners with research and risk teams, and develops engineering talent. Requires strong hands-on expertise in AI systems, cloud deployment, model optimization, and organizational leadership.

Build and deploy production AI/ML systems that optimize Samsara’s global hardware supply chain. Responsibilities include demand, inventory, cost, lead-time, supplier-risk, anomaly-detection, and cash-flow modeling; real-time inference pipelines; MLOps; data infrastructure; governance; and cross-functional AI adoption. The role also sets the supply-chain AI roadmap, mentors junior scientists, establishes modeling standards, and partners with Planning, Procurement, Finance, Engineering, and Operations.