Engineering Manager
Engineering Managers at AI companies lead engineering teams across the delivery cycle—hiring and developing engineers, setting technical direction in collaboration with senior ICs, owning roadmap and execution, and partnering with product and design counterparts. The work is the standard engineering management craft: 1:1s and growth conversations, architecture reviews and technical trade-off decisions, on-call and incident response, and translating cross-functional priorities into team plans. Technical scope varies widely—some EMs run platform and infrastructure teams, others run product engineering, others run ML or research-adjacent teams—but the canonical role is recognizable across software companies generally, with AI workloads as the specific domain rather than a different management discipline. These managers typically sit within engineering organizations as first-line or second-line leaders, reporting to directors or VPs depending on team size.
Skills
What companies are looking for in this role.
Building and leading high-performing engineering teams through hiring, onboarding, and talent development
Coaching, mentoring, and fostering professional growth of direct reports and emerging leaders
Setting technical direction and multi-year roadmaps aligned with business objectives
Managing cross-functional stakeholder collaboration with product, design, research, and infrastructure teams
Defining architecture decisions and technical strategy for complex systems at scale
Driving operational excellence, reliability, and performance of production systems
Establishing engineering processes, standards, and best practices across teams
Managing complex, multi-quarter projects spanning multiple teams and organizational boundaries
Delivering measurable improvements to shared infrastructure and platform capabilities
Designing and scaling data infrastructure, pipelines, and visualization platforms
Applying sound software engineering principles to domain-specific problems in networks, security, and distributed systems
Building and optimizing machine learning infrastructure for model training, deployment, and monitoring
Developing evaluation metrics, benchmarks, and quality signals for AI models and agents
Implementing applied AI techniques including prompt engineering, retrieval-augmented generation, and agentic frameworks
Building automation and self-healing systems for large-scale infrastructure management
Integrating evaluation systems into decision-making cadences for product launches and model training
Balancing ambiguity, available data, execution speed, and quality in decision-making
Communicating technical concepts and strategy effectively to diverse stakeholder groups
Steering teams through rapid change and evolving priorities with adaptability
Translating customer requirements and pain points into technical product solutions
Technology
The tools and technologies that define this role.
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280 open Engineering Manager jobs across 70 companies.
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