At Tecton, we solve the complex data problems in production machine learning. Tecton’s feature platform makes it simple to activate data for smarter models and predictions, abstracting away the complex engineering to speed up innovation.
Tecton’s founders developed the first Feature Store when they created Uber’s Michelangelo ML platform, and we’re 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 that’s 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.
Why you should take this role
Tecton is the industry’s leading feature platform - used by Data Scientists and ML Engineers at ML powerhouses like Atlassian, Block and Coinbase.
By taking on this role, you will be responsible for the most important part of Tecton: The core interfaces and capabilities that our primary end users (Data Scientist and ML Engineers) use every day to drive success with AI in their companies.
You will work directly with ML Platform teams & use case teams. This will expose you to the bleeding edge of production AI applications that have immediate impact on our customers’ bottom lines. You will learn how ML is applied across use cases as varied as recommendation systems, search ranking, fraud detection, anti money laundering, price predictions, safety predictions, personalization, credit underwriting and much much more.
Further, Tecton is actively expanding beyond Predictive ML and building a unified Data Platform for AI. In this role, you will be in the unique position to help devise a platform that caters to the MLE and DS needs to develop & productionize predictive and generative AI applications.
You will directly contribute significant revenue opportunities by unlocking new use cases for our customers.
Detailed Responsibilities
- Own the strategy and roadmap for making Tecton the most developer friendly platform for ML Engineers
- Work closely with customers & engineers to launch impactful new capabilities at a rapid pace
- Partner with Product Marketing to educate the market on why Tecton is a clear choice for predictive AI and generative AI use cases
Qualifications: Must have
- 5+ years of combined SWE or Product Management experience
- 2+ years of Product Management experience
- 2+ years of experience building either
- Comfortable interfacing directly with enterprises
-- user experiences for technical products that cater to ML Engineers or Data Engineers
-- OR applied ML applications
Qualifications: Nice-to-have
- Experience working in a startup environment
- 2+ years experience building product for Enterprise customers
Tecton values diversity and is an equal opportunity employer committed to creating an inclusive environment for all employees and applicants without regard to race, color, religion, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or other applicable legally protected characteristics. If you would like to request any accommodations from the application through to the interview, please contact us at [email protected].
This employer participates in E-Verify and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S.
Top Skills
What We Do
Founded by the team that created the Uber Michelangelo platform, Tecton provides an enterprise-ready feature store to make world-class machine learning accessible to every company.
Machine learning creates new opportunities to generate more value than ever before from data. Companies can now build ML-driven applications to automate decisions at machine speed, deliver magical customer experiences, and re-invent business processes.
But ML models will only ever be as good as the data that is fed to them. Today, it’s incredibly hard to build and manage ML data. Most companies don’t have access to the advanced ML data infrastructure that is used by the internet giants. So ML teams spend the majority of their time building custom features and bespoke data pipelines, and most models never make it to production.
We believe that companies need a new kind of data platform built for the unique requirements of ML. Our goal is to enable ML teams to build great features, serve them to production quickly and reliably, and do it at scale. By getting the data layer for ML right, companies can get better models to production faster to drive real business outcomes.
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