Engineering Manager, Ads ML Efficiency in United States at Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Engineering Manager, Ads ML Efficiency based in the United States.

This role sits at the core of improving how large-scale machine learning systems are trained, deployed, and optimized within a high-traffic advertising ecosystem. You will lead a specialized engineering team focused on making ML training and inference faster, more cost-efficient, and more reliable across ads ranking and recommendation systems. The work spans model optimization, GPU and infrastructure efficiency, performance tooling, and launch-readiness frameworks that directly impact production-scale ML systems. You will partner closely with ML platform, ranking, and serving teams to identify bottlenecks and deliver measurable performance improvements. This is a highly cross-functional and technical leadership role that blends hands-on ML systems understanding with strategic engineering direction. Your impact will be visible in reduced latency, lower compute cost, and faster iteration cycles for ML teams. The role is ideal for someone who thrives at the intersection of applied ML, systems engineering, and organizational leverage.

• Lead and grow a high-performing team of ML engineers focused on model efficiency, optimization, and ML systems performance across ads ranking and recommendation use cases
• Define and drive the technical roadmap for training optimization, inference acceleration, GPU utilization, and ML efficiency tooling
• Deliver measurable improvements in model training time, serving latency, infrastructure cost, and production launch reliability
• Build and scale internal systems for profiling, benchmarking, observability, cost analysis, debugging, and efficiency certification of ML models
• Partner with ML platform, ranking, and serving teams to identify system bottlenecks and accelerate high-priority model launches
• Establish engineering best practices around performance measurement, experimentation rigor, and production readiness for ML systems
• Balance short-term optimization work with longer-term platformization and automation of efficiency workflows
• Act as a key technical and organizational connector across multiple teams to ensure alignment on ML efficiency goals and execution
• Drive continuous improvement in system reliability, scalability, and operational excellence for production ML workloads

• Strong background in machine learning engineering with deep understanding of training, inference, debugging, and production ML systems
• Hands-on experience improving ML system performance, including training loops, serving infrastructure, or model optimization at scale
• Proven experience leading engineering teams, including hiring, mentoring, prioritization, and delivery management
• Strong understanding of distributed systems and trade-offs between cost, performance, scalability, and reliability
• Experience working with large-scale ML systems such as recommender systems, ranking models, or ads systems
• Ability to collaborate effectively with cross-functional stakeholders including engineers, PMs, and technical leadership
• Strong communication skills with the ability to clearly articulate technical decisions and trade-offs
• Experience in ads, marketplace ML, or similar large-scale production ML environments strongly preferred
• Familiarity with GPU optimization, distributed training frameworks, or performance tuning is a strong plus
• Experience building tooling for ML benchmarking, evaluation, or launch certification is highly desirable

• Comprehensive healthcare coverage including medical, dental, and vision insurance
• 401(k) retirement plan with employer matching contributions
• Equity compensation in the form of restricted stock units (RSUs)
• Flexible vacation policy and paid volunteer time off
• Generous paid parental leave and family planning support
• Mental health, coaching, and wellbeing programs
• Gender-affirming care and inclusive healthcare benefits
• Remote-friendly work environment with flexibility based on role and location
• Professional development support and global employee assistance programs
• Competitive base salary ranging from $230,000 to $322,000 USD, with potential equity and additional compensation based on performance and level