Apply modern machine learning techniques to advertising use cases such as inventory forecasting, pricing, targeting, and efficient ad delivery
Design, implement, and iterate on ML solutions from experimentation through production deployment and ongoing optimization
Build and scale ML architectures that balance model quality, latency, throughput, reliability, and cost
Design and maintain feature pipelines and feature stores supporting both real-time inference and offline training
Own major components of the model lifecycle, including experimentation, validation, deployment, monitoring, and iteration
Analyze experimental results and partner with product and engineering stakeholders to support data-informed decisions
Ensure models are observable, debuggable, and explainable in production environments
Implement monitoring for model performance, drift, bias, and overall system health
Contribute to engineering excellence through high-quality code, sound system design, and operational best practices
Provide technical guidance through code reviews, design discussions, and knowledge sharing

Bachelor's degree in Computer science or related field of study
5+ years of software engineering experience
Minimum 3 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 modern 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
Proven ability to thrive in a fast-paced, data-driven, and collaborative environment.

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

Senior Machine Learning Engineer

Key skills