Applied Science Manager, GameLift
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 be responsible for upholding the highest standards of scientific excellence, including rigorous experimental design, disciplined model selection, and reproducible evaluation methodology, while maintaining the speed and inventive culture of a startup operating within a large organization. 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 be equally comfortable debating the merits of deep learning architectures in a technical review as they are presenting a product roadmap to senior leadership, and will thrive in an environment where building something new from zero to one is the daily expectation.
Key job 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.
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 be responsible for upholding the highest standards of scientific excellence, including rigorous experimental design, disciplined model selection, and reproducible evaluation methodology, while maintaining the speed and inventive culture of a startup operating within a large organization. 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 be equally comfortable debating the merits of deep learning architectures in a technical review as they are presenting a product roadmap to senior leadership, and will thrive in an environment where building something new from zero to one is the daily expectation.
Key job 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.