Software Engineer, UI

Posted 12 Days Ago
Hiring Remotely in San Francisco, CA
Remote
165K-231K Annually
3-5 Years Experience
Machine Learning
The Role
As a Software Engineer, UI at Tecton, you will be responsible for building intuitive and scalable user interfaces to empower customers in managing and monitoring their machine learning workflows effectively. You will collaborate with cross-functional teams to deliver high-quality UI features and components that simplify data processes and support data-driven innovation.
Summary Generated by Built In

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.


As a member of the Tecton UI team, you'll help build intuitive and scalable user interfaces that empower our customers to manage and monitor their machine learning workflows effectively. Our UI aims to make complex data processes accessible and user-friendly, enabling users to focus on what matters most. You will collaborate closely with Product Managers, Designers, and Backend Engineers to deliver high-quality, scalable UI features and components that support our mission of simplifying data-driven innovation.

Responsibilities

  • Deliver High-Quality Product UI: Support our Product and Engineering teams by delivering high-quality UI features and capabilities to customers.
  • Drive Collaboration and Velocity: Work effectively with Product, Design, and Backend teams to drive velocity and deliver Tecton at scale.
  • Component Library: Help build our component library, creating extensible, reusable, pure components.
  • API Layer: Contribute to and help grow and champion our GraphQL API service.
  • Improve Reliability and Testing: Strengthen our codebase by adding Unit tests and E2E tests.
  • Mentor and Lead: Lead by example and mentorship both of UI Engineers and Tecton as a whole, fostering a culture of continuous learning and high-quality development practices.

Qualifications

  • Expertise in Core UI Engineering Languages: Highly proficient in JavaScript, HTML, and CSS fundamentals.
  • Advanced React Knowledge: Extensive experience with React and its ecosystem, including hooks, context, and performance optimizations. (or other UI framework)
  • Product-Focused Mindset: Proven track record of shipping and iterating on user-facing UI, collaborating effectively with Product Managers, Designers, and Backend Engineers.
  • Experience with Data-Heavy APIs: Skilled in working with complex, data-heavy APIs, defining API contracts, and building robust data integration layers.
  • Component Library Expertise: Demonstrated experience building and working with a first-class component library, understanding its impact on scalability and developer productivity.
  • Solid Understanding of Web Fundamentals: Strong knowledge of how browsers interact with servers, including making HTTP/HTTPS requests, handling responses, and rendering web content. Familiar with web security principles such as CORS, HTTPS, and authentication.
  • Strong Communication Skills: Communicates clearly and effectively in both written and verbal forms, maintaining professionalism and clarity in all interactions.
  • Attention to Detail: Meticulous in coding, testing, and reviewing, ensuring that no important details slip through the cracks.
  • Organized and Proactive: Ability to plan, organize, and manage time and priorities effectively, balancing immediate tasks with long-term objectives.
  • Adaptable and Curious: Willingness to help the team and company outside of strict product and design requirements, demonstrating flexibility and eagerness to learn.

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

JavaScript
React
The Company
HQ: San Francisco, CA
88 Employees
Hybrid Workplace
Year Founded: 2019

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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