Senior Machine Learning Engineering Manager

Why Safety AI Systems

A career at Roblox means you'll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone.

Why Join Us?

Roblox is committed to fostering a workplace where everyone feels valued, supported, and able to contribute. We are dedicated to diversity and inclusion and provide equal opportunities to all employees and applicants for employment.

What You'll Be Doing

• Own the vision, technical direction, and execution of machine learning solutions for the Multimodal Safety AI system, ensuring these systems effectively detect and prevent harmful content at scale.
• Lead and grow a high-performing team of ML engineers, fostering a culture of innovation, technical excellence, accountability, and inclusivity, while mentoring and developing talent.
• Break down ambitious long-term goals into an actionable, iterative roadmap - delivering continuous improvements in stages and driving tangible value at each step.
• Architect and guide the development of large-scale machine learning models with innovative architectures, ensuring they achieve high quality and are production-ready.
• Drive alignment on complex technical decisions across multiple teams (within and beyond the Safety org), demonstrating empathy and building consensus among diverse stakeholders.
• Collaborate cross-functionally with Product, Data Science, Policy, Design, and Operations partners to define and prioritize the machine learning roadmap for multimodal safety initiatives, ensuring alignment with broader Safety and Roblox objectives.
• Stay ahead of emerging trends in AI/ML and content moderation techniques, continuously innovating our safety approaches to anticipate new challenges.

Who You Are

• Proven track record of designing, developing, and launching ML models from scratch into production.
• Expertise in solving complex ML modeling, data, and infrastructure challenges - with a focus on maintaining high quality and velocity at scale.
• Ability to thrive in ambiguity: you excel at bringing clarity and direction to undefined or open-ended problem spaces.
• Demonstrated success collaborating across functions (e.g. Product, Design, Data, Research), working together to drive meaningful business and user impact.
• Strong product sense: able to establish clear success metrics and craft strategic roadmaps to achieve those goals.
• High emotional intelligence: adept at resolving conflicts, mentoring engineers, and nurturing the growth of your team members.
• Experience with modern microservice architectures and distributed systems programming paradigms (e.g. cloud services, scalable data pipelines).
• Hands-on to dive into code/architecture and guide technical discussions when needed, in addition to high-level planning.

Qualifications

• 5+ years of experience building large-scale machine learning systems in production environments.
• 2+ years of hands-on experience with vision language models or other foundation model technologies.
• Independent and self-directed: capable of charting a course with minimal guidance, and comfortable making decisions in uncertain situations.
• Committed to diversity & inclusion: dedicated to fostering a workplace where everyone feels valued, supported, and able to contribute.
• Passionate about cutting-edge ML tech: knowledgeable about the latest advances in AI/ML and excited to apply these technologies to keep users safe.

What You'll Get

The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on this page.

Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).