• Explore, shape, and develop new product experiences alongside cross-functional partners (design and product); from ideation to implementation at scale • Collaborate with product, design, and other stakeholders to identify and document requirements • Strengthen the team’s partnerships with key stakeholders in Engineering, Design, and Product to make ambitious product feature concepts a reality. • Lead investments into new technical capabilities that push the envelope and unlock product innovation • Collaborate with engineers to plan and sequence the delivery of new features and capabilities, making necessary trade-offs, while effectively communicating the delivery plans and celebrating successful launches • Partner with peer engineering teams to integrate new features that impact different areas within the product • Recruit and nurture exceptional engineering talent by investing meaningful one-on-one time to understand their motivations and team dynamics, while also creating a tailored career development plan. • Foster an inclusive team culture and environment that encourages collaboration, technical excellence, and innovation via calculated risks and product mindset.

• Hire, lead, and support a high-performing Infrastructure Platforms team, creating an environment where GitLab team members can do their best work and deliver strong results. • Connect business goals and customer needs with sound engineering by partnering closely with Product Managers and other Engineering Managers to define and deliver scalable infrastructure platforms. • Own agile delivery for your team, including planning, prioritization, and execution of projects that improve how we operate GitLab.com, GitLab Dedicated, and self-managed offerings. • Guide the security, reliability, performance, and scalability of core platform components, working with your team to design, review, and iterate on solutions. • Collaborate across Infrastructure Platforms, other Infrastructure groups, Support, and Customer Success to ensure our platforms meet the needs of teams that run and support GitLab. • Dive into technical discussions and reviews, helping to unblock complex issues and translating technical trade-offs into clear context for non-technical stakeholders. • Encourage continuous improvement in how we run our services, including quality, observability, incident response, and operational efficiency. • Participate in the Incident Management on-call rotation, partnering with reliability and development teams to help ensure availability goals for GitLab.com are met.

• Own and articulate the technical strategy and multi‑quarter roadmap for Enterprise Platform APIs, tools, and data models, aligning outcomes with enterprise objectives and retailer needs. • Lead, mentor, and grow a high‑performing team of 10+ software engineers; hire exceptional talent, provide coaching and feedback, and foster an inclusive, high‑trust, high‑ownership culture. • Design and deliver high‑scale, low‑latency APIs and distributed services (REST/gRPC); define clear API contracts, versioning policies, and robust schema management. • Partner cross‑functionally with Product, Business Development, Professional Services, Data Science, and peer engineering teams to prioritize impact, land cross‑org initiatives, and drive platform adoption across stakeholders. • Oversee production services end‑to‑end; establish engineering best practices and SLOs/SLAs, guide on‑call operations, lead incident response and postmortems, and raise the bar on reliability and operational excellence. • Elevate developer velocity and product quality through great tooling, CI/CD, observability, and pragmatic use of AI to automate toil and accelerate delivery.

• Lead execution of the ML roadmap, with a focus on building scalable, production-grade ML Platform infrastructure for 3D deep learning and computer vision applications. • Build and lead a high-performing ML Platform team, with an emphasis on reliability, reproducibility, and efficient model lifecycle management. • Partner with product, engineering, and research stakeholders to translate ML innovations into robust, deployable systems that support key business objectives. • Oversee the end-to-end deployment of computer vision models that extract structure from complex 3D scan data, ensuring production readiness and performance at scale. • Drive experimentation and implementation of SOTA techniques for 3D generative AI, while ensuring they are integrated into robust ML Platform workflows. • Collaborate cross-functionally to identify pain points and bottlenecks in current ML pipelines and deliver improvements through automation and tooling. • Manage large-scale data pipelines, versioning, and labeling workflows to support both generalized and fine-tuned model development. • Define and implement rigorous evaluation and monitoring strategies to maintain high model quality, mitigate drift, and ensure reliability in production. • Establish and maintain best practices for CI/CD of ML models, including model versioning, rollback mechanisms, and monitoring for performance and integrity.