At
Tecton, we solve the complex data problems in production machine learning. Tectonβs feature platform makes it simple to activate data for smarter models and predictions, abstracting away the complex engineering to speed up innovation.
Tectonβs founders developed the firstΒ
Feature StoreΒ when they created Uberβs Michelangelo ML platform, and weβre now bringing those same capabilities to every organization in the world.
Tecton is funded by Sequoia Capital, Andreessen Horowitz, and Kleiner Perkins, along with strategic investments from Snowflake and Databricks. We have a fast-growing team thatβs distributed around the world, with offices in San Francisco and New York City. Our team has years of experience building and operating business-critical machine learning systems at leading tech companies like Uber, Google, Meta, Airbnb, Lyft, and Twitter.
Drive excellence and product fit for Tectonβs primary user personas, the ML Engineer and the Data Scientist. Perform research with Tecton users and the broader market to understand and support these personas. Through both dedicated engineering resources and cross-team collaboration, build and refine the product workflows for exploring, developing, testing, and productionizing features across Tectonβs framework, CLI, SDK, API surface and GUI. Champion the Data Scientist and MLE personas at Tecton. Work with Product Marketing and DevRel to describe, promote, and evangelize Tecton as an ideal solution for feature engineering.
$162,000 - $222,000 a year
The estimated US base salary range for this position isΒ $162,000 - $222,000Β annually for employees based within California & New York. In addition to base salary, we offer competitive equity & comprehensive benefits such as medical, dental, vision, life, 401(K), flexible paid time off, 10 paid holidays each calendar year, sick time, leave of absence as per the FMLA and other relevant leave laws. Individual compensation packages are based on multiple factors such as location, level, role scope, and complexity, as well as additional job-related factors such as skills, experience, and expertise.
San Francisco / New York City: This role will need to be in the office for in-person collaboration 2-3 times per week