Machine Learning Engineer (Ad Tech)

Quick Facts

• Role: Machine Learning Engineer (Ad Tech)
• Focus: Ad science models, open-source LLM infrastructure, scalable training/inference systems
• Priorities: performance, cost efficiency, reliability at massive scale

Description

You’ll develop and optimize ad science models, open-source LLM infrastructure, and the underlying systems that power them—continuously improving performance and cost. The work spans creative optimization, user personalization, targeting, yield management, traffic shaping, and generative AI adoption, with accelerator-based training and reinforcement learning advancements. You’ll collaborate with data and platform engineering and with Applied Scientists on experiment design and modeling.

Responsibilities

• Build and support training pipelines and model implementations to maximize experimentation velocity
• Adapt open-source LLM infrastructure (e.g., DeepSpeed, OpenRLHF) to meet specific post-training needs
• Optimize online and batch inference for low latency and cost efficiency
• Create monitoring and evaluation solutions to ensure infrastructure reliability and correct model behavior
• Strengthen data pipelines using features across the product portfolio to enable new training and inference capabilities
• Explore new accelerator/compute platforms (e.g., JAX on TPUs)
• Collaborate with Applied Scientists on active research and contribute to experiment design and modeling

Requirements

• Master’s degree in machine learning or related field required (PhD a plus)
• 3+ years of experience as a Machine Learning Engineer building state-of-the-art systems at extreme scale
• Expertise in large-scale distributed data systems, including high-performance relational and key-value stores
• Experience with data orchestration (e.g., Airflow) and Spark data transformations
• Strong Python; familiarity with Java (especially for high-performance serving systems) is a plus
• Experience with PyTorch or a similar ecosystem
• Experience with accelerator-based inference systems (e.g., Triton) and optimization backends (e.g., ONNX, TensorRT)
• Experience with cloud platforms and Kubernetes

Benefits

• Competitive base salary (USD 172,500–210,000) and RSUs (where applicable)
• High-quality medical, dental, and vision insurance (including company-matched HSA)
• 401(k) company match
• Generous time off and company-wide holidays
• Substantial maternity and paternity leave and compassionate work environment
• Flexible working hours
• Wellness stipend
• Free lunch in office daily
• Pet-friendly environment and pet insurance
• Employee Assistance Program (EAP)