Top Engineering Jobs in Boston, MA

Lead and oversee multiple multidisciplinary teams to architect, develop, deploy, monitor, and maintain full-stack AI solutions (including LLM integrations). Provide strategic and technical leadership, drive experimentation and CI/CD practices, ensure quality and stakeholder alignment, promote innovation, and mentor senior and junior team members to deliver business-aligned AI outcomes.

Develop, test, and validate Generative AI agents and data pipelines with automated testing and governance controls. Analyze complex problems, produce actionable insights, mentor junior associates, and collaborate with multidisciplinary teams to deliver AI-enabled client solutions while upholding professional standards and documentation.

Provide regulatory compliance guidance and cybersecurity risk management to clients. Conduct compliance risk assessments and audits, implement frameworks (ISO/IEC 27001, NIST CSF), develop compliance programs, analyze data for insights, lead training on AML and financial crime prevention, and build client relationships while upholding professional standards.

Lead a team that builds and operates SDLC tooling and AI-enabled developer infrastructure. Own delivery, reliability, adoption, reporting, audit automation, sensitive-data protections, and governance for LLMs and agentic tooling. Partner with Architecture, Security, Privacy, and Engineering Excellence to translate standards into workflows, measure outcomes, and enable developer training and documentation. Hire, coach, and manage DevOps engineers, tooling admins, and developers to drive measurable engineering productivity improvements.

The Platform Engineering Product Director will lead enterprise integration strategy and middleware platforms, focusing on scalability, performance, and integration solutions across various business functions, while mentoring teams and managing vendor relationships.

The Director of R&D Mechanical Engineering at SharkNinja will lead the engineering team, shape R&D strategy, drive innovation, and champion technical excellence across multiple product development programs.

The Data Engineer will design and build data solutions, develop ELT jobs, ensure data quality, and collaborate with technical teams to align data strategies with business goals.

Lead and grow an AI engineering organization building foundation-model training and LLM-powered production systems. Own design, deployment, optimization, evaluation, governance, and observability of scalable AI platforms; make build-vs-buy decisions across open source and SaaS AI technologies; drive technical vision and mentor engineering teams to deliver responsible, high-performance AI capabilities.

Lead delivery of scalable, secure, production-grade AI and application platforms. Define architecture, CI/CD, observability, and quality standards; manage ~30 FTEs and contractors; drive cross-functional integration with data, product, and platform teams; ensure reliable model deployment, monitoring, and continuous improvement while communicating trade-offs to senior leadership.

Work on production-scale observability and security systems across backend, frontend, infrastructure, and data engineering. Design, build, and optimize distributed, cloud-native services; tackle performance, reliability, and scalability challenges; and deliver end-to-end technical projects while collaborating with mentors and peers.

Lead a small engineering team owning identity & access architecture and roadmap (SSO/OIDC/SAML, SCIM, multi-tenant isolation, authorization) across Tulip's multi-cloud environment. Stay hands-on in design and code, enforce automated tests and runbooks, partner with Security, Infrastructure, Product and Customer Success, and drive adoption of AI tooling in the SDLC to accelerate safe, reliable delivery.

Lead and execute SharkNinja's AI integration across the engineering lifecycle: build unified engineering data infrastructure, deploy AI-powered tooling for design, testing, reliability, and program management, embed AI Fellows into teams, drive change management and adoption, and deliver measurable outcomes tied to faster, higher-quality product development.

Lead and scale Sales Engineering teams in North America, defining strategies and collaborating with global stakeholders to drive technical sales success.

Co-op supporting early-stage product development by generating user-centered concepts, building and testing rapid prototypes, running design studies and user tests, and iterating designs with cross-functional teams. Emphasis on mechanical design, ergonomics, materials, and feasibility from concept through prototyping.

Design, build, and operate foundational platform services to improve availability, scalability, latency, and efficiency. Enable product teams via platform primitives (infra, databases, queuing), leverage Python, Go, AWS, Kubernetes, and Terraform, and mentor engineers to deliver resilient, scalable systems and improved developer experience.

Lead and mentor a team of Data Engineers to deliver scalable customer-focused data solutions. Provide hands-on technical ownership, set architectural and engineering standards, advance AI-enabled development practices, and collaborate cross-functionally to improve data foundations, reporting, and delivery.

Lead and scale global security engineering across application, AI, cloud, corporate, SecOps, and red team domains. Define secure architecture and governance, drive automated threat detection and AI security, personally perform adversarial testing, coordinate incident response and regulatory readiness, and report security posture to executives while building a high-performing team.

Lead strategy and execution for a next-generation Strategic Trade Management platform. Define architecture, drive cloud-native modernization, build and mentor engineering teams, partner with trading, risk, and business leaders, champion AI-enabled delivery, and ensure regulatory, resiliency, and operational excellence across trade lifecycle, pricing, risk, and settlement capabilities.

Join SharkNinja's Quality Assurance team as a full-time co-op to execute manual tests on consumer and connected products, develop test cases and protocols, document defects, collaborate across teams, participate in sprints and standups, and help improve testing processes and troubleshooting for firmware-based devices.

Lead and grow a platform engineering team to modernize build systems, CI/CD, developer tooling, cloud-native infrastructure, IaC, automation, and AI-enabled engineering capabilities. Drive delivery of strategic platform initiatives, improve developer productivity and platform reliability, manage technical debt, and partner cross-functionally to align engineering priorities with business goals.

Manage onshore and offshore software engineers to deliver scheduled releases. Coordinate with Architects, Product, QA, and Release Management; drive integrations, interoperability, security remediation, and incident response for distributed, microservices-based web applications.

Lead engineering for Cloud Observability across multiple cloud providers, managing ~40 engineers via Engineering Managers. Shape roadmap with product leadership, drive AI adoption, navigate cross-team dependencies, build and retain talent in NYC/Boston/Paris, mentor EMs, and participate in on-call rotations.

Lead enterprise architecture for engineering excellence: define architectural vision, standards, governance, and reference patterns; align roadmaps to business objectives; drive adoption of modern engineering capabilities across cloud, data, integration, and security; chair design authority, mentor architects, measure architecture value, and support critical escalations and urgent IT incidents.

Lead a team to operate and scale CarGurus' data platform across ingestion, orchestration, storage, and observability. Own roadmap, SLAs, cost, security, and quality controls; improve developer experience; collaborate with product and stakeholders to prioritize initiatives, resolve incidents, and drive platform reliability and scalability.

Lead design and prototyping of AI-enabled solutions for Oncology R&D. Develop data pipelines, feature engineering, analytics, and ML models on healthcare data. Partner with scientific and product teams to deliver usable tools, emphasize rapid iteration and production-readiness, and contribute to engineering standards and documentation.