You will design and build machine learning systems that power enterprise search, retrieval, recommendations, and AI agents. You will develop evaluation frameworks and metrics to measure the quality, performance, and business impact of LLM-powered applications, and build scalable data and ML pipelines that process large-scale AI interaction data. You will apply statistical modeling, experimentation, and causal inference to guide product development, analyze product usage and customer behavior, and prototype, evaluate, and deploy new AI capabilities using foundation models, embeddings, and retrieval-augmented generation techniques. You will define key performance indicators and build analytical frameworks that inform product strategy, while mentoring junior team members and establishing best practices across data science and machine learning.
Responsibilities
- Design and build machine learning systems that power enterprise search, retrieval, recommendations, and AI agents
- Develop evaluation frameworks and metrics to measure the quality, performance, and business impact of LLM-powered applications
- Build scalable data and ML pipelines that process and analyze large-scale AI interaction data
- Apply statistical modeling, experimentation, and causal inference to guide product development and strategic decision-making
- Collaborate with Product, Engineering, and Design to translate research and insights into production features
- Analyze product usage and customer behavior to identify opportunities for improving AI adoption, productivity, and user experience
- Prototype, evaluate, and deploy new AI capabilities using foundation models, embeddings, and retrieval-augmented generation techniques
- Define key performance indicators and build analytical frameworks that inform product strategy and company-wide decisions
- Contribute to the technical direction of the AI platform by identifying new opportunities to leverage machine learning and generative AI
- Mentor junior team members and help establish best practices in data science, experimentation, and machine learning
Requirements
- 5+ years of experience in Data Science, Applied Machine Learning, or a quantitative research role, or 3+ years with a PhD
- Degree in Computer Science, Statistics, Mathematics, Machine Learning, Economics, Physics, or another highly quantitative field
- Strong proficiency in Python and SQL, with experience building scalable data and ML pipelines
- Deep understanding of statistics, experimentation, causal inference, and predictive modeling
- Experience developing and deploying machine learning models in production
- Experience working with large-scale datasets and modern data infrastructure
- Familiarity with LLMs, embeddings, retrieval systems, RAG, or AI agents
- Strong analytical thinking with the ability to translate ambiguous problems into measurable solutions
- Excellent communication skills and the ability to work cross-functionally with engineering, product, and leadership
- Comfortable operating in a fast-paced, high-ownership startup environment
- Experience building or evaluating LLM-powered products or AI applications
- Background in search, recommendation systems, information retrieval, or knowledge graphs
- Experience designing A/B tests, offline evaluations, and ML benchmarking frameworks
- Familiarity with vector databases, embedding models, and agent orchestration frameworks
- Contributions to open-source ML projects or publications in machine learning, NLP, or related fields
- Experience in B2B SaaS, developer tools, or enterprise AI products
Benefits
- Paid Time Off – 21 vacation days plus 7 sick days plus 8 observed U.S. company holidays
- Health Coverage – 100% employer-paid medical, dental, and vision options for you and your dependents
- FSA and HSA options depending on selected health insurance plan
- Wellness Programs including OneMedical, Teladoc, Talkspace, and EAP
- 401(k) with a 100% company match on the first 6% contributed
- Pre-tax commuter benefits