Applied Science Manager, GameLift

About the role

We are seeking an Applied Science Manager to lead a new business unit on the GameLift team focused on creating AI and ML-based applications for the gaming industry. This leader will own the technical vision, scientific rigor, and end-to-end delivery of applied science initiatives that solve complex problems in machine learning, data science, and live-service gaming at scale. The ideal candidate operates at the frontier of AI research and deployment, translating ambiguous business opportunities into production-grade ML systems that generate measurable customer and commercial impact.

This role demands a hands-on technical leader who can define and execute an applied science roadmap while managing and mentoring a high-performing team of builders, scientists, and ML engineers. You will partner with engineering, product, and business stakeholders to bring AI-powered products from research through production deployment, meeting customer requirements and delivery timelines. The successful candidate will thrive in an environment where building something new from zero to one is the daily expectation.

Responsibilities

• Define and execute the technical roadmap for applied science initiatives, balancing frontier research with production delivery requirements and customer timelines
• Lead rigorous model development processes including algorithm selection, offline evaluation, A/B testing design, and statistical significance assessment, ensuring every production decision is grounded in scientific evidence rather than intuition
• Architect scalable, reusable ML platforms and inference systems designed to serve multiple products without proportional increases in staffing or rebuild cycles, enabling the team to move fast across a growing portfolio
• Manage and develop a team of applied scientists and ML engineers, providing technical mentorship, career growth opportunities, and performance management while maintaining a high hiring bar
• Drive end-to-end AI deployment from research prototyping through production inference, owning latency, availability, and cost targets alongside model quality metrics
• Establish and enforce scientific standards across all team projects, including peer review mechanisms, documentation requirements, and reproducibility practices that ensure technical decisions withstand scrutiny
• Partner cross-functionally with product managers, software engineers, and business leaders to translate customer problems into well-scoped technical solutions with clear success criteria
• Maintain a builder roadmap that sequences product launches against customer commitments, managing dependencies and communicating tradeoffs to stakeholders when scope or timeline pressure arises
• Enable the team to operate with startup-level autonomy and speed of invention while maintaining the operational discipline required for production systems serving customers at scale
• Stay current with developments across the AI/ML research landscape, identifying opportunities to apply new techniques

A day in the life

Your morning starts with production system health checks: inference latency, model freshness, experiment dashboards. Mid-morning you lead a technical design review, challenging your scientists on model complexity tradeoffs and coaching toward disciplined, phased approaches that maintain rigor without sacrificing speed. After lunch you join a cross-functional sync with product and engineering to align on delivery milestones, working through scope tradeoffs when customer requirements shift. Late afternoon is for people leadership: one-on-ones focused on career growth, reviewing hiring scorecards to keep the bar high, and scanning recent research for techniques your team can apply next quarter. Every day blends science, product delivery, and team building.

Requirements

• 3+ years of scientists or machine learning engineers management experience
• Knowledge of ML, NLP, Information Retrieval and Analytics
• Knowledge of machine learning approaches and algorithms
• Experience building complex highly-scalable systems that involve predictive models or applications of machine learning
• Experience communicating with users, other technical teams, and management to collect requirements, describe software product features, and technical designs
• Experience in building machine learning models for business application

Preferred Qualifications

• Experience building machine learning models or developing algorithms for business application
• Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers

Pay

USA, CA, San Diego - $183,800.00 - $248,700.00 USD annually

USA, NY, New York - $202,200.00 - $273,600.00 USD annually

USA, WA, Seattle - $183,800.00 - $248,700.00 USD annually

Benefits

• Sign-on payments and restricted stock units (RSUs)
• Health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans)
• Employee Assistance Program (EAP) and Mental Health Support
• Medical Advice Line
• Flexible Spending Accounts
• Adoption and Surrogacy Reimbursement coverage
• 401(k) matching
• Paid time off
• Parental leave

Learn more about our benefits at amazon.jobs/en/benefits.