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Software Engineer, Realtime Execution

Tecton

Saas

51-200

San Francisco, CA, USA

About the job

Overview:

At Tecton, we solve the complex data problem in production machine learning. Tecton’s feature platform makes it simple to activate data for smarter models and predictions. Tecton abstracts 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 Tecton's Realtime Execution team, you will help build the architecture that powers our online ingestion and serving infrastructure. You will impact Tecton’s ability to onboard high throughput real-time ML applications like recommendation systems. You will also have the opportunity to work on our low latency real-time features that power critical fraud and risk ML applications.
This position is open to candidates based anywhere in the United States. You can work in one of our hub offices in San Francisco or New York City or work fully remotely from outside those areas within the US.
Responsibilities

Develop and build critical high-performance solutions to scale our serving platform to millions of requests per second.
Design and build our data ingestion pipelines, both batch and streaming, focusing on scalability, minimal latency, and robust fault tolerance.
Take full responsibility for the entire lifecycle of your projects, starting from conceptualization and solution identification through design, implementation, testing, and ensuring smooth deployment to production.
Assess and prioritize tasks, demonstrating a keen awareness of performance-critical areas.

Qualifications

5+ years of experience in programming, debugging, and performance tuning distributed and/or highly concurrent software systems.
Degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience, with strong proficiency in building high throughput infrastructure.
Experience with Python, Kotlin or Go.
Experience with at least one of AWS, GCP.
Experience with low latency online storage like DynamoDB, Redis, and BigTable.
Experience with Data warehouses like Snowflake, BigQuery, Object Storage like S3
Experience with Streaming infrastructure like Kafka and Kinesis.