Staff Machine Learning Engineer, Ads Foundational Representations

About this role

Responsibilities

• Providing technical leadership and mentorship to MLEs in the team: driving designs & their review, establishing best practices in analysis, modeling and engineering, keeping the bar high.
• Working closely with team/org leadership developing technical strategy for content-based embeddings & relevance for Ads.
• Developing new or iterating on existing embedding models for advertising use cases, ranging from aggregation pipelines to two-tower architectures and sequence models.
• Working with local and 3rd-party LLMs/VLMs: extract representations, develop evaluation methodologies, prompt tune and fine-tune large models to build state-of-the-art embeddings.
• Building data processing and inference pipelines for the models we develop.
• Qualitative and quantitative evaluation of the various features we develop, end-to-end experimentation from internal benchmarks to downstream recommender system offline metrics to online experiments.
• Ensuring the reliability, scalability, and performance of the ML systems by writing automated tests, monitoring performance, and implementing best practices for model management.
• Participating in modeling and coding reviews: You will review work by other team members and provide feedback to ensure that it meets the team's standards for quality and performance.
• Collaborating with cross-functional teams to understand business requirements and translate them into technical solutions.
• Required Qualifications:
• 7+ years of hands-on experience with the full lifecycle of designing, training, evaluating, testing, and deploying industry-level models.
• Demonstrated Staff-level technical leadership: mentoring engineers, driving standards and bar raising, leading complex cross-functional projects: from requirements, design to cross-team/functional alignment and execution without direct people-management authority.
• Excellent communication skills, with the ability to translate complex technical concepts to different audiences, both verbally and in writing.
• Strong track record of working on content rich NLP/CV problems at scale, and using embeddings as a tool to solve them.
• Established data-driven approach for ML system development. Excitement about working with data and readiness to look behind the metric numbers.
• Familiarity with the Ads domain and/or Search/Recommender systems.
• Experience with mainstream DL frameworks: PyTorch or TensorFlow.
• Preferred Qualifications:
• Experience with our stack (Python, Pytorch, Airflow, BigQuery, Ray, k8s, kafka, GCP)
• Tech leadership experience: mentoring junior engineers and leading complex projects.
• Hands-on experience with using/fine-tuning/building LLMs.