Senior Machine Learning Engineer (AI)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
TL;DR
Senior Machine Learning Engineer (AI/Adtech): Designing and optimizing CTR/CVR prediction models for a real-time bidding system with an accent on signal discovery, feature pipelines, and large-scale decisioning. Focus on reducing latency under 100ms, automating experiment-debugging workflows, and bridging data-to-model pipelines to drive business growth.
Location: Must be based in Menlo Park, California, United States
Salary: $200,000 - $260,000 USD (Region A)
Company
builds AI-powered advertising solutions to democratize access to advanced machine learning for businesses worldwide.
What you will do
- Own production CTR/CVR prediction models end-to-end, including modeling, evaluation, and feature pipelines.
- Discover new data sources and contextual features to improve model performance.
- Design offline evaluations and conduct online A/B tests for rapid iteration.
- Develop agentic tooling to automate experiment debugging and signal discovery.
- Define technical direction and align with data and pipeline teams to optimize signals.
- Ensure data quality and maintain train/serve consistency in feature pipelines.
Requirements
- 5+ years of machine learning experience shipping production-grade models in business-critical environments.
- Proven track record with large-scale prediction or decisioning systems such as CTR/CVR, ranking, or recommendation.
- Proficiency in Python and data analysis.
- Ability to function effectively under ambiguity.
- Must be based in the United States.
Nice to have
- Experience using LLMs as agents or feature extractors for evaluation, experiment debugging, or signal discovery.
Culture & Benefits
- Comprehensive medical, dental, and vision insurance.
- 401(k) plan with company match.
- Flexible Time Off (FTO) and scheduled paid holidays.
- Culture defined by humility, an uncapped growth mindset, and inclusive collaboration.
- Focus on delivering durable value through intelligent risks and disciplined trade-offs.
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