Lead the design and delivery of machine learning solutions across advertising use cases such as inventory forecasting, pricing, targeting, and efficient ad delivery
Apply modern machine learning techniques to solve complex, real-time advertising problems
Provide technical leadership for ML system architecture, modeling approaches, and production readiness within your domain
Design, build, and scale ML architectures that balance model quality, latency, throughput, reliability, and cost
Oversee the full ML lifecycle for owned systems, from experimentation through production deployment and iteration
Design and maintain feature pipelines and feature stores supporting both real-time inference and offline training
Partner with product and engineering stakeholders to translate requirements into clear technical plans and measurable outcomes
Interpret experimental results and guide data-informed decision-making
Ensure ML systems are observable, debuggable, and explainable in production
Establish and maintain monitoring for model performance, drift, bias, and system health
Champion engineering excellence through best practices in code quality, system design, testing, and operational reliability
Mentor and support engineers through code reviews, design discussions, and ongoing technical guidance

Bachelor's in Computer Science or equivalent practical experience
7+ years of software engineering experience
5+ years of hands-on experience developing and deploying machine learning systems in production
Strong knowledge of machine learning fundamentals, mathematics, and statistics
Experience operating ML systems in low-latency, high-throughput environments
Strong communication and collaboration skills with both technical and non-technical partners
Solid foundations in algorithms, data structures, and numerical optimization
Proficiency in Python (primary), with experience in Java and SQL
Experience with ML frameworks and tooling such as TensorFlow, PyTorch, and Hugging Face
Experience with one or more of the following: Deep learning methodologies (e.g., sequence-based or representation learning models)
Transformer architectures (e.g., BERT, GPT, ViT) for NLP and/or vision
Multimodal embedding techniques across text, image, audio, or structured data
Large language models and related evaluation methodologies
Retrieval-augmented generation (RAG) architectures
Experience building systems on cloud-native infrastructure and distributed platforms

A bonus and/or long-term incentive units may be provided as part of the compensation package
Full range of medical, financial, and/or other benefits, dependent on the level and position offered

Lead Machine Learning Engineer

Key skills