Software Engineer, Realtime Execution

**Software Engineer, Realtime Execution** Remote (US) / San Francisco, CA / New York, NY / Toronto / Seattle, WA Engineering – Software Development / Full-time / Remote At , 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 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. $189,000 - $258,000 a year The estimated US base salary range for this position is $189,000 - $258,000 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.

Founded by the team that created the Uber Michelangelo platform, Tecton provides an enterprise-ready feature platform to make world-class #machinelearning 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.