Senior Machine Learning Engineering Manager

About the role

This role leads safety-focused AI and machine learning efforts for Roblox, shaping real-time multimodal content understanding at massive scale. The position defines technical vision, owns critical services, and aligns engineering solutions with product and policy goals to protect the community.

Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.

Key facts

What you'll do

Architect and guide multimodal model development to achieve production-ready quality for safety systems.

Define the technical roadmap for multimodal safety AI, breaking down long-term goals into iterative, value-driven phases.

Implement large-scale machine learning models and pipelines to detect and mitigate abusive behavior proactively.

Requirements

5+ years of experience building large-scale machine learning systems in production environments.

Proven ability to design, develop, and launch ML models from scratch into production.

2+ years of hands-on experience with vision language models or other foundation model technologies.

Expertise in solving complex ML, data, and infrastructure challenges while maintaining high quality and velocity at scale.

Ability to thrive in ambiguity, bringing clarity and direction to open-ended problem spaces.

Demonstrated success collaborating across functions such as product, design, data, and research to drive user impact.

Strong product sense, establishing clear success metrics and crafting strategic roadmaps.

High emotional intelligence, resolving conflicts and mentoring engineers to support team growth.

Experience with modern microservice architectures and distributed systems programming paradigms like cloud services and scalable data pipelines.

Hands-on capability to dive into code and architecture while guiding technical discussions, alongside high-level planning.

Practical notes

Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Machine learning roles in safety systems often require close coordination between engineering, policy, and trust teams to define acceptable behavior boundaries.

Production ML systems at scale demand robust data pipelines, monitoring, and iterative improvements to maintain reliability and performance.

Technical leadership in AI safety involves balancing innovation speed with risk management and regulatory considerations.

Cross-functional collaboration and clear success metrics are essential for aligning machine learning initiatives with business and user safety goals.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.