Staff ML Engineer - Advertising

Requirements

• Master’s or PhD in Computer Science, Software Engineering, Mathematics, or a related field.
• Experiences in building and scaling production-grade ML systems, with a strong track record of delivering impactful solutions in complex environments.
• System-Level ML Expertise: Proven ability to design, deploy, and operate large-scale ML systems, including pipelines, online services, and experimentation frameworks.
• Strong Technical Foundations: Expertise in data structures, algorithms, and distributed system design, with the ability to make high-quality architectural tradeoffs.
• Programming & ML Stack Proficiency: Strong coding skills in Python, Scala, or similar languages. Hands-on experience with modern ML frameworks (e.g., PyTorch, Hugging Face, Ray, vLLM) and large-scale data processing tools (e.g., Spark, Hadoop).
• Data & Analytical Excellence: Exceptional ability to analyze large datasets, perform deep dives, and translate findings into actionable improvements. Strong SQL skills and experience with experimentation and segmentation.
• Influence & Communication: Excellent communication skills with the ability to articulate complex technical concepts, influence partners, and drive alignment across teams.

What You'll Be Doing

• Lead End-to-End ML Systems at Scale: Architect, build, and evolve large-scale machine learning systems powering ad ranking, recommendations, and advertiser optimization, with ownership over system design, reliability, and long-term technical direction.
• Drive Strategic, Data-Informed Decisions: Leverage large-scale production data to identify high-impact opportunities, define ambiguous problem spaces, and influence product and business strategy through data-driven insights.
• Own and Elevate the ML Lifecycle: Set best practices across the full ML lifecycle—feature engineering, model development, evaluation, deployment, and monitoring—while improving robustness, reproducibility, and scalability of production pipelines.
• Collaborate Across Functions to Shape Solutions: Partner closely with product, engineering, and research to translate complex business objectives into scalable ML solutions, influencing roadmap and prioritization through technical expertise.
• Optimize System Performance at Scale: Define, own, and evolve key system metrics (e.g., relevance, revenue, latency, reliability). Lead efforts to improve system efficiency, scalability, and cost-performance tradeoffs across high-throughput environments.
• Advance ML Innovation in Production: Drive adoption of state-of-the-art approaches (e.g., deep learning, GenAI, LLM-based systems) and translate modern technology into practical, high-impact production systems.
• Provide Technical Leadership and Mentorship: Mentor engineers, lead design reviews, and set engineering standards. Act as a technical anchor for the team, setting a higher standard on ML engineering excellence and experimentation rigor.

Nice to Haves

• No specific nice to haves were mentioned in the provided job description.

Perks and Benefits

• No specific perks and benefits were mentioned in the provided job description.