Engineering Manager, AI Engineering

Magnet Forensics

2d•CA$160,000 - CA$210,000•Hybrid

About The Position

Magnet Forensics is looking for an Engineering Manager to build, develop, and partner with a growing team of 5-10 ML Engineers working on AI systems that power their digital forensics capabilities. The role involves hiring, coaching, career development, and performance management. The manager will be responsible for raising the bar on AI/ML craft by shaping standards, developing engineers, and building a strong community of practice. The ML Engineers are distributed across Canada and Sweden and work in cross-functional groups alongside Software Engineers and SDETs, organized around missions like Search and Enrichment. As part of the AI Engineering leadership team, the manager will collaborate with peers to build cohesion, partner with Tech Leads on technical challenges, and work with Product and UX to ensure the team is solving the right problems for users. The team tackles challenging and meaningful problems, such as building AI to accelerate case work and improving search functionalities for investigators.

Requirements

3+ years managing or leading teams of engineers, with a track record of building healthy teams, delivering results, and coaching people through growth.
Hands-on experience building applied AI/ML systems, sufficient to understand what good looks like, evaluate technical judgment, and have credible conversations on technical topics.
A genuine coaching mindset, finding satisfaction in others' growth, giving direct and useful feedback, and having coached Senior and Staff-level engineers.
Sound technical judgment to navigate complex decisions, weigh trade-offs, and help teams move toward adaptable, maintainable solutions.
Strong cross-functional instincts, communicating clearly with engineers, PMs, UX, and leadership.
Experience working effectively across distributed teams, understanding how to build cohesion when people are not in the same room.
Familiarity with AI-assisted development tools and a perspective on how to effectively integrate them into engineering workflows and team practices.
Bachelor's degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.

Nice To Haves

Experience in high-stakes domains (security, healthcare, legal) where correctness matters.
Experience with AI/ML in constrained deployment models (on-prem, edge, air-gapped).
Experience building and operating scalable, distributed systems.
Familiarity with MLOps tooling (e.g., experiment tracking, model versioning, CI/CD for ML).

Responsibilities

Drive operational excellence and accountability for team shipments, continuously improving work processes through efficient, high-quality delivery frameworks.
Protect team autonomy by providing clarity and minimizing decision-making complexity.
Guide technical execution by staying close to the work, asking sharp questions, spotting risks, and helping the team weigh trade-offs.
Partner with Tech Leads to ensure complex initiatives have the right direction and systems are adaptable and aligned with long-term goals.
Coach and grow engineers by helping them scope ambiguous projects, navigate technical trade-offs, and prepare for high-stakes conversations.
Create opportunities for engineers to take ownership and expand their impact, providing clear, actionable feedback.
Foster a culture of experimentation where the team can safely share and test ideas, measure results, and learn quickly.
Lead with context and collaboration, aligning roadmap goals with team capacity and ensuring the team solves the right problems for users.
Nurture a high-performing, distributed team by evolving processes and communication patterns to enhance collaboration and trust.
Leverage time zone coverage in Canada and Sweden as an advantage.
Drive thoughtful AI-assisted development practices, evaluating AI development tools for acceleration versus risk and raising standards accordingly.
Cut through the noise to determine what AI development tools add real value versus hype.