Top AI Jobs in Boston, MA

Leads enterprise AI tooling and security engineering, designing scalable cloud platforms and secure AI applications. Responsibilities include protecting models, agents, prompts, data, APIs, and outputs; securing the AI/ML lifecycle; implementing identity, encryption, monitoring, threat detection, adversarial testing, and incident response; and establishing engineering standards. The role mentors engineers, influences architecture and technology strategy, and partners with governance, risk, compliance, and business teams to support responsible AI adoption.

Own the full sales cycle for Fieldguide’s AI audit platform across public sector organizations, initially focusing on federal accounts. Build territory and go-to-market strategy, navigate complex government procurement processes, exceed revenue targets, develop executive and practitioner relationships, and manage multithreaded enterprise deals. Collaborate cross-functionally to shape messaging, sales processes, and market expansion into state, local, and higher education sectors. The role is fully remote in the United States with up to 30% travel.

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Lead full‑stack development of React Native mobile apps and Node.js backends for digital therapeutics. Own architecture, build CI/CD and AWS infrastructure, design multi‑agent AI workflows, mentor engineers, and resolve cross‑cutting production issues.

Lead product strategy and execution for signals exploitation and tracking capabilities. Define roadmaps, shape backlogs with customers and product owners, support architecture and design reviews, and drive development and demonstration of advanced algorithms and software for DoD and Intelligence Community missions. Balance reuse, integrated system offerings, and component supply-chain opportunities while leading engineering teams through the Agile lifecycle and supporting technical marketing and business development.

Own the end-to-end integration and automation architecture for the MyCDW platform. Design ServiceNow integrations, APIs, event-driven patterns, middleware, data contracts, AI-agent guardrails, automated workflows, and observability. Establish governance, security, coding standards, testing, documentation, and reusable architecture patterns. Evaluate platforms, lead prototypes, resolve complex issues, manage vendors, influence stakeholders, and align solutions with business strategy, compliance requirements, scalability, resilience, and cost objectives.

Build and lead enablement programs for Customer Success and Implementation teams, including onboarding, product training, continuous learning, playbooks, and AI-enabled workflows. Identify performance gaps, translate product updates into practical training, improve adoption of tools and processes, and establish feedback loops with Product, Support, Customer Education, and Product Marketing. The role supports stronger renewals, expansions, customer outcomes, and operational efficiency in a growing SaaS organization.

Serve as a global SME for Industrial Metaverse technologies (PLCs, IoT, cloud/edge, digital twins, AI, EMC/RED, cybersecurity). Lead standards work, develop test programs, perform and supervise complex testing, train staff, represent UL externally, and support clients and continuous improvement across regional or global labs.

The Distinguished Engineer will drive innovation in banking technology, lead software engineering projects, mentor teams, and utilize AI and advanced architectures for risk management.

As a Staff Product Manager, you'll drive the strategy and success of SIEM products, collaborating with various teams to fulfill customer needs and market demands while focusing on security for SMBs.

Lead and operationalize GitLab's Internal Audit function: plan and execute risk-based audits, oversee SOX testing, facilitate enterprise risk management, coach a distributed audit team, implement QA/improvement programs, and report to senior leadership and the Audit Committee.

Design, build, deploy, and support large-scale AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, and observability. Collaborate cross-functionally, optimize model performance and cost, and contribute to technical vision and roadmap for foundational AI platforms.

The role involves developing AI-powered products, conducting applied research, collaborating with a cross-functional team, and building AI foundation models. Candidates should be innovative, technical, and have a deep understanding of AI methodologies.

Architect complex embedded hardware and software systems for next-generation audio products. Evaluate feasibility, tradeoffs, scalability, and system constraints across power, compute, memory, latency, performance, and security. Develop simulations, prototypes, AI agents, and automated engineering workflows for architecture exploration and verification. Translate audio, sensing, machine learning, and AI technologies into reusable platforms, frameworks, algorithms, and IP while providing principal-level technical leadership across engineering teams, suppliers, and partners.

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.

Own strategy and execution for Klaviyo's Analytics AI product area, principally the Analytics Agent. Define quality measurement, AI-generated analytics UX, and query infrastructure transitions. Partner cross-functionally to ship data-grounded LLM experiences, ensure metric accuracy and platform health, write rigorous product specs, and support multi-brand Enterprise analytics needs.

The Applied Researcher II will work on developing AI foundation models, partner with cross-functional teams, and engage in impactful research to enhance customer experiences using advanced AI techniques.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale production AI systems for scalability, latency, throughput, cost, and hardware utilization. Partner with engineers, scientists, technical program managers, and product managers while contributing to technical vision, roadmap development, and responsible AI practices. Lead and mentor engineering teams and apply emerging AI research to production systems.

Serve as a Microsoft Dynamics 365 functional lead delivering CRM/ERP assessments, design, configuration, fit-gap analysis, data migration, testing, training, and client-facing implementation support. Lead project tasks, create functional requirements and process flows, support proposals and business development, and coach junior staff to deliver solutions leveraging Microsoft platforms.

Lead Microsoft Dynamics 365 CRM implementations, architect solutions, manage project lifecycles and client accounts, mentor teams, ensure quality and post-go-live support, and drive innovation and continuous improvement.

Lead design and implementation of enterprise AI architectures and integration solutions, manage multiple projects and executive client relationships, drive strategic technology planning and business development, mentor teams, conduct market research and feasibility studies, and promote technological innovation across the firm.

Lead and coach teams to design, develop, and deliver production-grade AI and data solutions for clients. Manage client accounts, ensure quality and timelines, analyze complex datasets, implement best practices, and drive continuous improvement while fostering client relationships and technical excellence.

Design, code, test, and maintain applications using Kotlin, Rust, and SwiftUI. Implement ATDD and CI/CD, build API integrations and microservices, manage application lifecycle and project planning, conduct code reviews and debugging, collaborate with clients, and apply analytical thinking for business process improvements.

Lead large Microsoft Dynamics 365 (CRM/CE) implementations as a Senior Manager: engage senior clients, design functional solutions, oversee delivery lifecycle, coach teams, refine processes, and ensure operational excellence and adherence to firm standards.

Lead design and implementation of data architecture for healthcare systems, influence stakeholders, mentor teams, ensure regulatory compliance, enable data integration and transformation, and apply AI techniques to improve healthcare operations and reporting.