Join us in building the future of digital identity! With IDs in Wallet, users can use their IDs in person, in app, and on the web. We build the services that make this possible. Our services sit between Apple Wallet and digital credential issuers like state and national governments. We work at the intersection of distributed systems, cryptography, and ML, implementing cutting-edge identity features while maintaining the highest levels of security and user privacy. We're actively contributing to and shaping the digital identity ecosystem through standards like ISO 18013/23220 and the W3C Digital Credentials API. We are looking for an experienced engineering manager who is passionate about building high-performing teams, running ML services at scale, and thriving in a fast-paced environment. If you love solving meaningful problems that impact millions of people and defining the technical direction of critical services, we want to hear from you!
Description
As an Engineering Manager on the Wallet Identity Services team, you will lead a group of engineers and technical leads responsible for the services powering liveness detection, face matching, and image quality. You will own the full lifecycle of these ML-powered services — from architecture and development through deployment, monitoring, and on-call operations. This role sits at the intersection of software engineering, machine learning, and DevOps. You will partner with ML engineers to deploy and operate models at scale, and collaborate closely with the iOS team that builds the on-device experience. You will define engineering strategy across multiple workstreams, drive operational excellence, and grow both your team and its leaders.
Responsibilities
Lead, mentor, and grow a team of engineers and technical leads building mission-critical identity verification services at scale. Define the engineering strategy and operational roadmap across multiple workstreams, aligning with organizational objectives and senior leadership. Own the end-to-end architecture and operations of ML-powered services — from design and implementation through monitoring and on-call — setting the standard for operational excellence. Partner with ML engineers and AI leaders to deploy and operate models, manage model lifecycle, and drive MLOps best practices. Collaborate with iOS client teams to define and evolve APIs connecting on-device experiences with server-side processing. Work with product, design, and cross-functional partners — including senior and executive stakeholders — to shape requirements, align priorities, and deliver cohesive user experiences. Translate product vision into technical roadmaps, establish delivery cadence, and ensure the team consistently delivers high-quality results. Champion a team culture of innovation, open communication, collaboration, and accountability.
Minimum Qualifications
10+ years of professional software engineering experience building and operating distributed systems at large scale. 5+ years of engineering management experience with a track record of building, growing, and leading high-performing teams. Experience managing managers or technical leads, with accountability for outcomes across multiple workstreams or sub-teams. Experience managing the deployment and operation of ML models in production, including model serving, monitoring, and lifecycle management. Strong understanding of distributed systems fundamentals and trade-offs in consistency, latency, and throughput. Demonstrated ability to drive operational excellence, including on-call culture, incident management, and reliability practices. Clear and thoughtful communicator, able to drive consensus on complex technical topics with diverse audiences including cross-functional and geographically distributed teams. A track record of building influence and healthy relationships within and beyond your immediate team.
Preferred Qualifications
Hands-on software development experience with Java or Kotlin and Spring. Experience building and operating high-volume REST or gRPC services. Familiarity with ML infrastructure — model serving, experiment tracking, data pipelines, and A/B testing for models. Experience with containerization, orchestration, and cloud-native architectures. Understanding of security, privacy, and cryptography fundamentals. Experience with databases at scale, both relational and NoSQL. Experience with workflow orchestration tools. Domain experience in computer vision or liveness detection. Familiarity with digital identity standards such as ISO 18013/23220. Experience using generative AI tools to accelerate software development.
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