Engineering Manager, Express AI Foundations

Adobe•San Jose, CA

22h•$146,300 - $289,900•Hybrid

About The Position

Adobe Express enables everyone — individuals and large organizations alike — to produce impressive content effortlessly. The AI Foundations team builds the flexible, scalable AI framework that powers creativity at scale across design, imaging, motion, and personalization. We are looking for an Engineering Manager to lead and grow a team of engineers building the AI infrastructure for Adobe Express. This role sits at the intersection of technical leadership and people management — you will own the delivery of the AI stack spanning Agentic AI, Imaging AI, Motion AI, and Personalization AI, while developing the engineers who build it. You will partner closely with product, research, and cross-functional engineering teams to shape the technical direction of one of Adobe’s most strategic platforms. This is an early-level people management role (M30), well suited to someone with a strong engineering foundation who has been leading & managing teams for 3–5 years and is looking to deepen their impact through the growth of others and the delivery of high-consequence systems at scale.

Requirements

3–5 years of engineering management experience, with a track record of delivering complex infrastructure or platform projects through a team.
A strong technical foundation in distributed systems, AI/ML infrastructure, or large-scale service development — enough to earn credibility with senior engineers and make sound architectural trade-offs.
Experience owning team execution end-to-end — including structured prioritization across competing workstreams, dependency management, and shipping reliably in an agile, fast-moving environment.
Can articulate trade-off decisions clearly, not just make them.
Clear, structured communication skills — able to translate technical trade-offs into business terms for PMs and non-technical stakeholders, and to influence direction without authority across teams.
Comfort navigating ambiguity: defining scope, making decisions with incomplete information, and adapting plans quickly as systems and priorities evolve.
Working fluency in modern AI/ML concepts — LLM orchestration, inference infrastructure, prompt engineering, AI output evaluation, and data pipelines — sufficient to guide technical decisions, set a quality bar, and grow team capability.
Demonstrated ability to grow engineers: coaching, setting expectations, giving actionable feedback, and supporting career progression at multiple levels.

Nice To Haves

Masters degree or equivalent experience in Computer Science, Machine Learning, or a related field.
Background as a hands-on engineer in data infrastructure, ML platform, or large-scale backend systems before moving into management.
Experience hiring and ramping engineers across a range of seniority levels, including senior and staff engineers.
Exposure to Generative AI development — LLMs, diffusion models, or multimodal systems — either as an individual contributor or as a manager overseeing such work.
Familiarity with MLOps practices: feature stores, model registries, evaluation pipelines, and deployment workflows.
Actively tracks emerging AI/ML trends and has a considered view on what’s applicable versus overhyped — and can bring that perspective into team planning conversations.
Awareness of security, data privacy, and responsible AI concerns specific to AI-backed systems — including bias, safety, and handling of user data in model pipelines.

Responsibilities

Own end-to-end delivery of AI infrastructure workstreams — including LLM orchestration, inference services, data pipelines, and evaluation frameworks — from planning through production.
Maintain strong enough technical depth to participate in system design reviews, challenge architectural decisions, and unblock your team on complex problems.
Work with your team to set and meet engineering quality standards: observability, fault tolerance, latency guarantees, security, and responsible AI practices — including bias awareness and data privacy for AI systems.
Make deliberate, explicit calls on technical debt versus feature velocity, and hold the team accountable to those decisions.
Develop and communicate a coherent technical roadmap for your area, balancing immediate delivery with sustainable long-term architecture.
Partner with product management & engineering leadership to decompose high-level product requirements into concrete technical requirements — breaking ambiguous asks into scoped workstreams with clear dependencies, effort estimates, and sequencing.
Drive prioritization across competing demands — balancing new feature work, infrastructure investment, and reliability improvements with a clear, defensible rationale the team and stakeholders can align on.
Collaborate with AI research, data science, and platform teams to integrate in-house and third-party models and APIs into production-quality serving systems.
Represent your team’s work and direction to senior stakeholders — communicating progress, risks, and technical trade-offs clearly to both technical and non-technical audiences.
Manage and grow a team of 6–10 engineers across varying seniority levels, providing regular coaching, feedback, and career development support.
Drive a strong hiring bar — own the full recruiting lifecycle for your team, from sourcing through offer, and help build an inclusive, high-performing culture.
Foster a collaborative, psychologically safe environment where engineers can do their best work and grow into senior and staff roles.