Top Platform Engineer Jobs in Boston, MA

The role involves building and improving WHOOP's data platform infrastructure, ensuring reliability and efficiency, and driving best practices across engineering and data workflows.

The Lead Platform Engineer will develop and manage Workday integrations, optimize HR technology, and lead a team while ensuring quality and innovation in various HR modules.

Design, build, and operate AI platform components (LLM gateway, model serving, developer tooling). Own features from development to production, contribute to reliability, observability, governance, and cross-functional integration while producing technical documentation and participating in design reviews.

Lead design and build of an AWS-only AI Operations platform: hosting, IaC, registries (Accountability, Agent, Tool), identity/authorization patterns, developer DX, observability, and security hooks for agentic systems. Evangelize internally and partner on compliance and security integrations.

Lead operational design and engineering for the data platform: own Snowflake and dbt administration, automate platform operations and CI/CD, monitor platform health and observability, enforce governance and access, optimize performance and costs, and develop AI/agent integrations to enable governed data access for business users.

The role involves designing and managing secure cloud infrastructure on AWS, automating security practices, and collaborating with various teams to improve developer workflows and ensure compliance. Key tasks include IAM management, implementing automation tools, and participating in incident response.

Lead Workday functional engineer responsible for designing, configuring, and supporting Workday HCM modules (HCM, Recruiting, Compensation, Payroll, Benefits). Partner with product owners, provide production support, automate processes, maintain documentation, and drive process improvements and Workday upgrades.

Lead ownership and evolution of Dropbox's Windows endpoint platform (CorpFleet). Improve endpoint management, Intune workflows, security/compliance, automation, operational reliability, and cross-functional rollout. Mentor engineers and drive multi-phase platform initiatives, documentation, and runbooks to scale secure, reliable endpoint experiences.

Design, build, and operate Jellyfish's Databricks Lakehouse and data/AI platform. Build ingestion, transformation, and delivery pipelines; govern data with Unity Catalog; enable BI, agentic analytics, metadata, lineage, and observability. Provide technical leadership, set architecture and standards, mentor peers, and collaborate with cross-functional stakeholders to make Databricks the foundation for analytics and AI.

As a Lead Platform Engineer, you will develop solutions on Workday for HR, improve data quality, and lead integration teams. Collaborate with stakeholders to ensure cohesive system configurations and optimize business processes using Agile methodologies.

Lead a team to develop solutions in Workday Finance, focusing on automation and integration, while collaborating with stakeholders and improving business processes.

Technical leader responsible for cloud platform architecture, IaC, CI/CD, self-service developer tooling, reliability, observability, and cost optimization. Mentor engineers, drive automation, lead initiatives from design to production, and collaborate with Security and Operations on secure scalable solutions and incident response.

As a Senior Lead AI Engineer, you will design and develop advanced AI systems, collaborate with cross-functional teams, and introduce innovative AI techniques while mentoring others.

The Senior Lead AI Engineer will collaborate with cross-functional teams to design, develop, and implement AI software solutions, focusing on large-scale production systems and AI model optimization.

As a Lead AI Engineer, you will develop, deploy, and support AI software components, collaborate with cross-functional teams, and optimize performance of large-scale AI systems.

The Lead AI Engineer will design, develop, deploy, and support AI software components, collaborating with cross-functional teams to create scalable AI solutions that enhance customer interactions and operational efficiency.

Design, build, and maintain full stack systems for WHOOP's design system and translation infrastructure, improving internationalization support and collaborating across teams.

Join the Search Platform team to design, implement, test, and deploy performant search and data-access features. Collaborate with product, data analysts, and engineers, run experiments, iterate rapidly, and provide production support for mission-critical applications serving millions of users.

As a Backend Software Engineer II, you will develop and maintain member-facing features using WHOOP's AI capabilities, collaborating with cross-functional teams and ensuring high-quality software solutions.

Design, develop and maintain backend systems for WHOOP's platform, ensuring scalability, security, and performance while collaborating across teams.

This role involves leading the development of AI-powered products, optimizing large scale AI systems, and mentoring cross-functional teams. It focuses on designing and deploying AI components, contributing to the technical vision, and applying cutting-edge AI techniques.

Lead AI Engineer responsible for developing AI-powered products and deploying scalable AI solutions using technologies like LLM and machine learning algorithms. Collaborate with cross-functional teams to optimize performance and support AI systems.

The Senior Platform Engineer will develop and support Workday solutions, collaborate with various teams, and enhance human resource technologies for Capital One.

Lead design, build, deploy, and support GenAI platform services (foundation model training, LLM inference, similarity search, guardrails, evaluation, observability). Optimize LLM performance and scalability, collaborate cross-functionally, contribute to technical vision and roadmap.

Design, build, and operate the core data warehouse, ingestion, orchestration, and cataloging platform. Develop batch and streaming pipelines, ensure data quality, governance, observability, and provide ML data platform capabilities. Lead architecture, improve platform reliability and performance, and collaborate with product, engineering, data science, security, and compliance teams.