Engineering Manager

Manage, coach and grow a team of engineers, providing regular feedback, career development while ensuring performance remains high.

Build a healthy team culture with clear ownership, sustainable pace and high standards for quality and craftsmanship.

Recruit and onboard new engineers as needed and develop technical leadership within the team.

Manage competing priorities and expectations across multiple stakeholder groups, building trust through fostering mutual understanding, delivering consistently and communicating transparently.

Represent the team's roadmap, capacity and risks in cross-functional planning and prioritization discussions.

Drive technical discussions and design reviews, with enough depth to understand the implications of key decisions on reliability, scalability and production outcomes.

Partner with engineers on architecture and design choices, asking the right questions rather than dictating solutions.

Ensure the team's technical decisions are well understood by, and defensible to, other stakeholders.

Model and champion effective use of AI tools across the team's day-to-day work, including AI-assisted research, administrative workflows, and code generation.

Help the team develop good judgment about how to balance the productivity gains AI can bring against the risks it introduces.

Continuously look for ways AI tooling can improve team efficiency, code quality, and decision-making, and share what's working with peers.

Manage effectively through the major changes AI is bringing to the industry.

Own delivery of the team's roadmap balancing feature work, technical debt and reliability commitments.

Establish and maintain appropriate quality, testing and monitoring practices.

Provide clear, proactive communication on progress, risks and blockers to stakeholders and leadership.

Define metrics as needed and develop an understanding of why we did or didn’t hit our targets.

5+ years working as a software engineer. Some of that experience should be in the ML sphere.

This should not be your first time managing and we’d prefer at least 4 years of experience managing engineers directly in an ML focused domain.

Comfortable engaging in technical discussions on architecture, data flows, and system design.

Demonstrable experience using AI tools in a professional capacity for research, administration and code generation with a clear point of view on how to use them responsibly and cost-effectively.

Strong knowledge of software development best practices, cloud computing platforms, big data technologies, MLOps toolkits, and SLO-driven reliability management.

Experience working with at least one of Databricks, Spark, Airflow, Vertex and the GCP technology stack in general is preferred.

Strong track record managing relationships with a diverse set of technical and non-technical stakeholders.

Ability to communicate clearly across audiences and influence without authority.

Comfortable navigating competing priorities and pushing back constructively when needed.

Genuine care for engineer growth and development, with experience giving structured feedback and building career paths.

Ability to build trust and psychological safety within the team while holding a high bar for output and accountability.

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