• You will lead a team working to build, scale, and optimize the machine learning infrastructure that powers Recursion's drug discovery platform. • The ML infrastructure team is responsible for building and operating platforms that allow data scientists and ML engineers to train, deploy, and monitor models across Recursion's massive datasets. • You will work closely with researchers and ML engineers to understand their infrastructure needs and build scalable solutions for model development, training, and deployment. • Act as a mentor, coach, and sponsor.

• Lead by example and manage a team of talented engineers, providing technical guidance, mentorship, and career development support; • Collaborate with cross-functional teams, including data science and data engineering managers, and product managers to define project goals, scope, and deliverables; • Drive the end-to-end development process, from design to implementation and deployment, ensuring high-quality and scalable solutions (with controlled cost); • Identify and prioritize technical initiatives, considering ROI, scalability, and performance requirements; • Implement modern software development practices, including agile methodologies, continuous integration, and deployment, to ensure efficient and effective project delivery; • Collaborate with stakeholders to gather and analyze requirements, ensuring that the platform meets customer needs and provides an exceptional user experience; • Foster a collaborative and inclusive team culture, promoting knowledge sharing, innovation, and continuous improvement.

Role Description We’re looking for an engineering leader to lead and grow a strong team of engineers, build relationships with customers internally and externally, and champion our vision of making Stripe’s risk management a feature that attracts and retains merchants, and becomes a product differentiator. This is an exciting opportunity to partner with teams across Stripe to build the best merchant experience, and contribute directly to Stripe’s growth. Responsibilities - Support the team in delivering a high level of technical quality and impact via APIs, user-facing experiences, services, and systems - Recruit, hire, scale, and develop an amazing team of engineers - Executing cross-functionally with leadership, product teams, infra teams & risk strategists - Be actively involved in strategic direction and platform decisions that impact all of Stripe and Stripe customers Qualifications - At least 3 years of engineering management experience - Prior experience as a Machine Learning Engineer or equivalent Requirements - Managed teams that can collaborate with product teams, respond rapidly to customer needs along with building technology and capabilities that are strategic and foundational in nature - Enjoy designing, measuring & improving user experience - Empathetic to customer needs but visionary enough to not just deliver a faster horse - Comfortable planning in quarters, and can set a vision for several years - Comfortable working with geographically distributed teams and remote workers

• Lead, mentor and grow a team of approximately four engineers, including a Principal Engineer and full-stack engineers • Run effective 1:1s, career conversations, and performance reviews using Grip's progression framework • Foster a culture of psychological safety, high standards, and continuous improvement • Own hiring and onboarding for the Manage Squad in partnership with Engineering leadership • Partner closely with the Product Manager and Designer to review upcoming initiatives, assess feasibility, and surface technical risks early • Own the squad's delivery cadence — sprint planning, prioritisation, and release quality • Balance feature delivery with ongoing investment in platform health: tech debt, refactoring, security, and governance • Take ownership of features from start to finish using agile methodologies, delivering with feature flags, tests, and observability • Champion high-quality technical communications: proposals, specs, testing reports, and release planning • Drive AI-first ways of working within the squad — embedding AI tooling into development workflows, code review, testing, and delivery • Drive architectural consistency, code quality, and engineering best practice within the Manage Squad • Work closely with the Head of Platform to align Manage's architecture with the wider platform vision • Identify and lead progressive modernisation of legacy systems — migrating and consolidating without disrupting delivery • Deliver features with security and performance in mind, embedding compliance and governance principles throughout • Collaborate with the VP of Engineering Operations on AWS infrastructure (EKS, ECS, Lambda, CloudFront, S3) as it relates to the Manage domain • Contribute to CI/CD pipeline improvements and progressive delivery practices across squads • Drive reliability monitoring and observability within Manage (Prometheus, Grafana, Sentry) • Contribute to security posture improvements: vulnerability scanning, pen testing coordination, and enforcement of standards • Work closely with the Engineering Director for Engage to align on shared engineering standards, cross-cutting initiatives, and resource decisions • Partner with the Head of Platform on cross-domain architectural decisions that span Manage, Data, and AI systems • Collaborate with the VP of Engineering Operations on delivery excellence, operational compliance, and cost governance • Represent the Manage Squad in engineering leadership forums and contribute to the organisation-wide technical strategy