24 days ago
Remote, United StatesStaff+
Responsibilities
- Build and improve machine learning models for campaign optimization, prediction, ranking, bidding, forecasting, calibration, and value estimation.
- Develop optimization algorithms that balance advertiser outcomes, spend delivery, cost efficiency, campaign goals, marketplace dynamics, and system constraints.
- Work with large-scale signals from auctions, impressions, clicks, video events, conversions, users, context, inventory, campaigns, and marketplace feedback.
- Design CTR, CVR, VCR, CPA, ROAS, app-install, user-value, and campaign-performance models.
- Develop bidding, pacing-aware optimization, ranking, exploration, and value-estimation approaches for performance advertising.
- Improve model calibration, online and offline evaluation, experimentation, observability, and production feedback loops.
- Address sparse conversions, delayed feedback, biased logs, cold-start campaigns, attribution noise, and online/offline metric mismatch.
- Define model-ready features, labels, attribution windows, negative examples, training datasets, and online serving requirements with signal engineers.
- Translate model outputs into real-time decisioning systems with engineering, product, analytics, and platform teams.
- Provide technical leadership and mentorship to engineers and applied scientists working on performance optimization.
Requirements
- At least 10 years of experience building production machine learning, ranking, recommendation, prediction, optimization, advertising, marketplace, bidding, or pricing systems.
- Experience building large-scale prediction or optimization systems in production.
- Strong understanding of supervised learning, ranking, calibration, causal thinking, experimentation, statistical evaluation, and model monitoring.
- Experience with CTR/CVR prediction, conversion modeling, bid optimization, value modeling, forecasting, calibration, or performance optimization.
- Experience working with large-scale data and distributed machine learning workflows.
- Strong engineering skills in Python, Java, SQL, Spark, TensorFlow, PyTorch, XGBoost, or similar technologies.
- Ability to reason about model quality, business impact, system constraints, production tradeoffs, and online performance.
- Ability to provide technical leadership across ambiguous, high-impact optimization problems.
- Bachelor’s, master’s, or PhD in Computer Science, Machine Learning, Statistics, Mathematics, Engineering, or a related technical field.
- Preferred experience in advertising, search, recommendations, marketplaces, e-commerce, fintech, pricing, bidding, or real-time optimization systems.
- Preferred familiarity with real-time bidding, programmatic advertising, ad serving, attribution, pacing, identity, incrementality, or performance advertising.
- Preferred experience with exploration and exploitation, counterfactual evaluation, uplift modeling, delayed-feedback modeling, biased logs, model observability, A/B testing, online experimentation, incrementality testing, or lift measurement.
- Preferred experience working cross-functionally with product, engineering, analytics, and business stakeholders.
Benefits
- Hybrid work schedule with 3 days in the office and 2 days working remotely; remote work may be considered for the right candidate.
- Paid leave programs and paid holidays.
- Healthcare, dental, vision, disability, and life insurance.
- Commuter benefits and physical and financial wellness programs.
- Unlimited DTO in the US.
- Mobile reimbursement, fully stocked pantries, and in-office catered lunches five days per week.
Tech Stack
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