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Principal Applied Scientist/Machine Learning Engineer
| Company | Rokt |
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| Location | San Francisco, CA, USA |
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| Salary | $315000 – $350000 |
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| Type | Full-Time |
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| Degrees | Master’s, PhD |
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| Experience Level | Senior, Expert or higher |
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Requirements
- PhD or Master’s in Computer Science, Statistics, Mathematics, or related field with specialization in ML, AI, or Information Retrieval.
- 10+ years of industry experience building production-grade ML systems.
- Deep expertise in at least two of the following: Reinforcement Learning (Bandits, Policy Optimization), Multi-Task Learning & Mixture of Experts (MMOE, PLE, DCN), Deep Learning Architectures (Transformers, Graph Neural Networks), Bayesian Methods & Probabilistic Modeling, ML for Ads, E-commerce, or Two-Sided Marketplaces (big plus!).
- Proven ability to drive impact across multiple teams and organizations.
Responsibilities
- Develop and implement machine learning models to solve high-impact business problems.
- Design and optimize scalable ML pipelines and infrastructure for production environments.
- Conduct applied ML research, prototype new modeling approaches, and rigorously test innovations.
- Evaluate and improve model performance, ensuring robustness, scalability, and interpretability.
- Translate complex business needs into practical ML solutions, collaborating with product and engineering teams.
- Stay ahead of emerging ML trends, contributing to knowledge sharing through tech talks, brown bags, and best practice evangelism.
Preferred Qualifications
- Familiarity with architectures or experience with models mentioned in the benchmark: DCNV2, MMOE, Deep & Wide, ESMM, xDeepFM, and GDCN.
