Software Engineer, Developer Infra

Reposted 11 Days Ago
3 Locations
In-Office or Remote
197K-237K Annually
Senior level
Machine Learning
The Role
Seeking a Developer Infrastructure Engineer with expertise in build systems and cloud infrastructure to enhance Tecton's software development capabilities.
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.

The Foundations team at Tecton is focused on creating building blocks that enable Tecton’s engineering organization to quickly ship high-quality software. Our focus spans a wide range of domains, from build systems and test infrastructure to the core frameworks and platforms that Tecton’s products are built upon. We collaborate across the engineering organization to identify friction points in building software at Tecton and build platforms, frameworks, and tools to solve them.

We are looking to bring on a Developer Infrastructure-focused engineer with strong experience in Build Systems (Bazel) and Cloud Infrastructure (AWS) to help us invest further in creating an amazing inner development loop for Tecton.

Responsibilities
  • Own and evolve our Bazel monorepo ecosystem. We have some very cool challenges that stem from supporting a several-million-LOC polyglot monorepo, maintaining multiple well-tested versions of our Python SDK, and more!

  • Ensure our build system integrates well with IDE journeys, including AI tooling.

  • Create a high-fidelity local development environment for a product that is focused on orchestrating cloud and third-party resources.

  • Empower the organization to “shift left” development and testing.

  • Improve the scalability, usability, and security of our CI infrastructure.

Qualifications
  • 5+ years of experience in designing, implementing, and maintaining software systems, with demonstrated expertise in build systems and developer tools.

  • Excellent communication and collaboration skills, and ability to write detailed technical specifications.

  • Strong familiarity with Bazel and its more powerful features (rules and rulesets, platforms and toolchains, configurable build attributes, etc)

  • Experience with at least one of AWS, GCP, or Azure (AWS experience strongly preferred)

  • Experience with the following technologies is preferred / a bonus, but we don’t expect every candidate to have experience with them all: Python, Kotlin, Kubernetes, gRPC, Postgres.

  • Degree in Computer Science or related field, or equivalent practical experience.

The estimated US base salary range for this position is $197,000 - $237,000 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.

Tecton is a remote-friendly company that employs a hybrid working policy for employees based in the SF, NY, and Seattle areas. We believe that working in-person helps us stay connected, collaborate faster, and promote a strong culture while still providing the flexibility of working from home. We expect SF & NY employees to be in the office at least two designated days per week, and those in the Seattle area at least two designated days per month.

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

AWS
Bazel
Grpc
Kotlin
Kubernetes
Postgres
Python
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The Company
HQ: San Francisco, CA
88 Employees
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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