2 days ago
Base Salary
$151k - $189k/yr
Responsibilities
- Build and improve machine learning models for search, recommendations, notifications, user understanding, and embeddings.
- Develop, test, and deploy models and supporting services in production.
- Use large datasets to create features, train models, and evaluate performance.
- Contribute to retrieval, ranking, personalization, and experimentation systems.
- Monitor production models and improve their quality, reliability, latency, and scalability.
- Partner with product, engineering, and data science teams to translate business and user needs into practical machine learning solutions.
- Participate in technical design discussions, code reviews, team planning, and development of model lifecycle standards and best practices.
- Use experimentation and marketplace metrics to measure the impact of the work.
Requirements
- At least 3 years of professional experience in machine learning, data science, software engineering, or a related field.
- Proficiency in Python and experience with scikit-learn, PyTorch, or TensorFlow.
- Experience building, evaluating, and deploying machine learning models in production.
- Familiarity with recommendations, search, personalization, ranking, NLP, deep learning, or LLMs.
- Understanding of classification, regression, ranking, feature engineering, and model evaluation.
- Experience with data pipelines, experiment tracking, model monitoring, or other parts of the machine learning lifecycle.
- Strong software engineering fundamentals and ability to write reliable, maintainable code.
- Ability to collaborate with engineers, data scientists, product managers, and other cross-functional partners.
- Ability to break down moderately complex problems, evaluate tradeoffs, and deliver solutions with support from senior team members.
- Extra credit includes experience with embedding-based retrieval, multi-stage ranking, graph-based models, recommender systems, large-scale datasets, high-traffic cloud-based production systems, generative retrieval, LLM evaluation, post-training, explainable AI, fairness, or responsible machine learning.
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, and vision coverage, mental health support, and a $500 wellness stipend; $2,000 learning stipend and ongoing development.
- Remote and office support includes internet and commuting assistance, free lunch, and gym access at the San Francisco office.
- Flexible PTO, 15 holidays, two flex days, team outings, and referral bonuses.