Build and iterate on LLM & AI-powered product features
Design, prototype, and ship features that use LLMs (e.g., content generation, summarization, classification, semantic search, assistants, recommendations).
Work with engineers to integrate LLMs into the product via APIs or internal services (RAG, tools/functions calling, workflows, pipelines).
Define evaluation strategies for LLM features (e.g., human-in-the-loop evaluation, rubrics, prompt experiments, offline/online metrics).
Continuously refine prompts, data pipelines, and system design based on user behavior, quality metrics, and product goals.
Own product analytics for data- & AI-powered features
Partner with product managers and designers to define success metrics (e.g., adoption, engagement, conversion, retention, quality, time-to-value).
Instrument new features: define events, ensure proper logging, and validate that data is correct and trustworthy.
Analyze funnels, cohorts, user journeys, and experiment results to understand drivers of behavior and outcomes.
Translate insights into clear recommendations that influence roadmaps, prioritization, and feature iteration.
Work with real-world transactional data (SQL & NoSQL) Explore, clean, and transform data from transactional (OLTP), analytical (OLAP), and event-based systems.
Work across SQL (e.g., Postgres, Snowflake) and NoSQL (e.g., Redis, document/Key-Value stores).
Design data assets and features that are usable by both analytics workflows and LLM/ML systems.
Define and track data quality metrics (completeness, consistency, timeliness, drift, schema changes).
Build checks, monitors, and alerts to detect data issues that can affect analytics or AI/LLM performance.
Work with data and engineering teams to diagnose root causes and drive changes that improve data quality over time.
Use core ML concepts (feature design, model evaluation, bias/variance, generalization) to reason about LLM and non-LLM approaches.
When appropriate, build and evaluate lighter-weight or traditional models (e.g., scoring, ranking, classification) to complement or replace LLM solutions.

Requirements

A track record of high ownership: taking responsibility for problems end-to-end, improving systems rather than just describing them, and pushing initiatives across product, engineering, and data.
A genuine love for messy, real-world data, and the curiosity to dig into anomalies until you understand what's really happening.
Hands-on experience with real-world transactional data in production environments, including messy, incomplete, or biased data.
Demonstrated experience improving data quality in production environments.
Demonstrated experience shipping LLM-based product features, such as: Using hosted LLM APIs or in-house models Designing prompts and workflows Evaluating and iterating on LLM behavior using real user data.
Experience in product analytics, including: Defining and tracking product KPIs and feature-level metrics Building and interpreting funnels, cohorts, and retention/engagement analyses Influencing product decisions and roadmaps with data-driven insights.
Experience measuring and improving data quality, and working with engineering to fix upstream issues.
Strong communication skills: ability to work cross-functionally and explain technical decisions and trade-offs to non-technical partners.
Strong SQL skills: complex joins, window functions, CTEs, and performance-aware querying.
Solid Python skills for data and AI work (e.g., pandas, NumPy, scikit-learn; OpenAI, Anthropic, and Gemini LLM libraries/frameworks).
Formal education in machine learning concepts, such as: Supervised/unsupervised learning Model selection and regularization Evaluation methodologies (train/validation/test splits, cross-validation, experiment design)

Benefits

Competitive compensation and equity packages
Health, dental, and vision insurance
Parental leave
Flexible vacation time
Wellness allowance
Technology allowance
Company-sponsored personal and professional development
L&D: Partnerships with Ethena and monthly Lunch & Learns
Wellbeing: access to many wellbeing perks, including Peloton, Fetch, OneMedical, Headspace care+, etc.
Caregiver Support: company seed into the dependent care FSA and company sponsored Care.com membership.
Regular performance reviews: Vouch conducts regular performance discussions with all team members, offering goal setting and check-ins, development discussions, and promotion opportunities

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills

LLMAISQLNoSQLPythondata qualityproduct analyticsmachine learningdata pipelinesfeature design

Soft skills

ownershipcuriositycommunicationcross-functional collaborationproblem-solvingdata-driven decision makinginfluencinganalytical thinkingadaptabilityattention to detail