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Founding ML/Data Science Engineer

Taro Verified

Sylvan Labs

Redefining how revenue teams grow after the first sale.
San Francisco, CA, USA
$150,000 - $250,000
Machine Learning
Mid-level
Hybrid
11-50 Employees
3+ years of experience

Taro Hiring Bonus Eligible

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Receive a cash bonus of up to $15,000 when you successfully land this role. You can view your bonus here.

Job Description

Sylvan Labs is seeking a Founding Full-Stack AI Engineer to redefine how enterprise companies drive revenue by building a revenue execution system for B2B enterprises. As a Founding Engineer, you'll work directly with the founders to architect, build, and scale the platform from 0 to 1. You'll take full ownership of core features, shape the product roadmap, and drive technical execution in an environment that values speed, iteration, and excellence. This role involves designing end-to-end ML systems, building feature engineering pipelines, developing hybrid ML/LLM systems, and owning the full modeling lifecycle.

The role requires experience with classical ML, deep learning, and LLMs, as well as production experience with model serving, feature stores, and training pipelines. You will be responsible for building churn and expansion prediction models and leveraging LLM agents to orchestrate and automate ML pipelines. Sylvan Labs offers equity and visa sponsorship. This is a great opportunity to join a lean, elite team and build a category-defining platform for post-sales enablement.


Responsibilities

  • Design end-to-end ML systems, from model selection and experimentation through production deployment
  • Build feature engineering pipelines that extract signal from both structured business data and unstructured text
  • Develop hybrid ML/LLM systems — knowing when to use traditional ML vs. language models based on the problem
  • Own the full modeling lifecycle: EDA, feature engineering, training, validation, and drift monitoring

Requirements

TypeScript
React
Python
  • Strong ML fundamentals (gradient descent, regularization, bias-variance tradeoffs)
  • Experience with the full spectrum: classical ML, deep learning, and LLMs
  • Production experience with model serving, feature stores, and training pipelines
  • Statistical rigor: experimental design, hypothesis testing, causal inference
  • Proven ability to ship end-to-end ML products

Benefits

Equity
Visa Sponsorship
  • Equity
  • Visa sponsorship available