6 days ago
Base Salary
$238k - $297k/yr
Responsibilities
- Define technical strategy and system design for ML models and infrastructure spanning search, recommendations, notifications, generative retrieval, and core embeddings, including build-versus-buy and platform decisions.
- Set technical standards and best practices for model development, experimentation, and production deployment while mentoring engineers and data scientists.
- Partner with engineering leadership, product, and data science to turn ambiguous business problems into technical roadmaps and align stakeholders on priorities and tradeoffs.
- Build and ship high-leverage models and systems hands-on and unblock the team on difficult technical problems.
- Own the roadmap for retrieval models and multi-stage ranking systems operating on a data platform with billions of data points.
- Help shape explainability, fairness, and quality practices for responsible AI across the product.
Requirements
- 8+ years of experience in machine learning, data science, or a related field, with end-to-end ownership of large-scale production ML systems.
- Deep expertise in Python and ML frameworks such as scikit-learn, PyTorch, or TensorFlow.
- Experience with recommendations, personalization, NLP, deep learning, LLMs, or explainable AI.
- Deep familiarity with experiment tracking, model monitoring, and feature pipelines at scale.
- Experience architecting and scaling ML infrastructure such as embedding-based retrieval, ranking systems, or GNNs in a high-traffic cloud production environment.
- Strong foundation in classification, regression, ranking, and model evaluation, with sound judgment about their application.
- Experience setting technical direction across teams and mentoring senior and mid-level engineers.
- Track record of driving measurable business impact through ML systems at scale.
- Experience with Generative Retrieval and LLM Post Training recipes is a plus.
- Clear, persuasive communication and the ability to align technical and non-technical stakeholders around a shared roadmap are valued.
- Experience building or scaling technical practices and standards from the ground up is extra credit.
Benefits
- Equity in a fast-growing company; 401(k) match, competitive compensation, and financial coaching; paid parental leave, fertility benefits, and parental coaching; medical, dental, vision, and mental health support; $500 wellness stipend; $2,000 learning stipend and ongoing development; remote-work support plus commuting, free lunch, and gym access at the San Francisco office; flexible PTO, 15 holidays, and 2 flex days; team outings and referral bonuses.