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Applied Scientist (AI)
130 000 - 170 000$
Описание вакансии
Текст:
TL;DR
Applied Scientist (AI): Building autonomous advertising systems that use reinforcement learning and related decision-making methods for real-time targeting, ad optimization, bidding, measurement, and personalization with an accent on contextual bandits, counterfactual evaluation, auction dynamics, and low-latency production systems. Focus on designing experiments, translating research into reliable models, and improving campaign performance, auction efficiency, and measurable business outcomes.
Location: Irvine or Los Angeles, California, United States
Base compensation: $130,000–$170,000 per year
Company
is an AI-powered, buy-side advertising platform focused on connected TV, identity resolution, content intelligence, and measurable advertising outcomes.
What you will do
- Develop, train, and evaluate reinforcement learning, contextual bandit, ranking, and prediction models for ad optimization, bidding, targeting, and personalization.
- Study auction dynamics, delayed feedback, exploration and exploitation, budget constraints, pacing, and reward design.
- Translate research concepts into production-ready models operating at high throughput and low latency.
- Design offline and online experiments, including counterfactual and off-policy evaluation, to measure model quality and business impact.
- Deploy, monitor, retrain, and improve models with engineering partners while addressing leakage, imbalance, drift, calibration, and market changes.
- Define objectives, labels, loss functions, reward signals, evaluation metrics, and delivery plans with scientists, engineers, and product partners.
Requirements
- 1–3 years of experience developing and applying machine learning models in production or research environments.
- Strong foundation in machine learning, deep learning, probability, statistics, and optimization.
- Practical experience with Python and frameworks such as PyTorch or TensorFlow.
- Experience or project exposure to reinforcement learning, contextual bandits, sequential decision-making, recommendation systems, online experimentation, or related methods.
- Ability to formulate machine learning problems, objectives, features, loss or reward functions, evaluation metrics, and experimental designs precisely.
- Experience analyzing large-scale data and communicating technical findings to scientists, engineers, and cross-functional partners.
Nice to have
- Experience with digital advertising, real-time bidding, audience modeling, ad ranking, personalization, or large-scale recommendation systems.
- Experience with distributed computing, cloud platforms, LLMs, generative AI, or multimodal AI.
Culture & Benefits
- Research-oriented environment with technical communication, code and model reviews, experimentation, and knowledge sharing.
- Fully paid health insurance.
- Paid parental leave.
- Unlimited paid time off.
- Focus on professional growth and employee well-being.