At [Tecton](http://tecton.ai/), 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](https://www.tecton.ai/blog/feature-stores-versus-feature-platforms-feast-tecton/) 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.

This role is part of our growing Security team, responsible for securing Tecton’s product and the company as a whole. You'll have the unique opportunity to shape our security practices from the ground up, ensuring our product remains resilient and protected against emerging threats. You will collaborate with cross-functional teams to embed security seamlessly into our products and protect not only Tecton, but also our customers.

### **Responsibilities**

- Work closely with Tecton’s DevOps team to implement security controls to satisfy CIS AWS and Kubernetes benchmarks.
- Define and implement security standards to secure Tecton’s SDLC.
- Assist DevOps team in improving Tecton’s Zero Trust access controls across cloud infrastructure.
- Assist in improving Tecton’s vulnerability management program.
- Triage emerging vulnerabilities and assess their impact on Tecton.
- Triage and manage vulnerability remediation submitted through Tecton’s vulnerability disclosure process.
- Assist Tecton’s DevOps team in creating a secure image pipeline for Tecton deployments.

### **Qualifications**

- 3+ years of experience in a security role.
- 1 - 3 years of experience in a vulnerability management program for a cloud-native, containerized environment.
- Experience with industry security frameworks such as NIST or CIS.
- Experience with infrastructure-as-code tools such as Terraform, Ansible, Puppet
- Experience securing AWS services
- Experience hardening Kubernetes deployments
- Fluent in one or more programming languages, such as Python or Golang
- Strong and effective verbal and written communication skills

DevSecOps Engineer

At [Tecton](http://tecton.ai/), 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](https://www.tecton.ai/blog/feature-stores-versus-feature-platforms-feast-tecton/) 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 staff-level engineer building Tecton platform team, you’ll play a critical role in architecting, designing, and scaling our Tecton’s platform-level building blocks for building the next generation of accessible ML infrastructure.

### **As part of this team, you will be working in one or more of the following areas to build the next generation of Tecton infrastructure:**

- Architecting APIs and abstractions over multiple cloud, storage, and compute platforms
- Building platforms that enable modular development and testing
- Transitioning Tecton’s control plane toward a multi-tenant architecture
- Explainability and debugging as a first-class property of every layer of the stack
- Fleet management and optimizations for hundreds (and eventually thousands) of Tecton deployments
- Partnering with Security on org-wide data privacy and security measures

### **Responsibilities**

- Own and lead large technical domains starting from the problem definition and technical requirements along with implementation and maintenance
- Lead multi-engineer projects of strategic importance to Tecton spanning cross-functional teams including design, product management, and other engineering teams
- Drive efforts to improve engineering practices, tooling, and processes along with mentorship for senior engineers
- Develop a deep understanding of the fundamental problems our customers face in building ML systems
- Be a generalist as needed. We’re a small, but growing engineering team and each engineer needs to be versatile
- Reinforce Tecton’s [“Fast, but Focused”](https://www.tecton.ai/blog/evolving-culture-and-values/) core value

### **Qualifications and Values**

- 7+ years of experience in building product software systems
- 2+ years of technical leadership experience for a group of engineers
- Experience working in large Python, Java, Kotlin, or Go codebases and running cloud-native production systems using Kubernetes, AWS, and Docker
- Experience with distributed systems, SQL, and NoSQL databases
- Bias to action and passion for delivering high-quality solutions
- Strong communication and ability to write detailed technical specifications
- Excitement about coaching and mentorship of junior engineers
- BSc, MS, or PhD in Computer Science or related fields

Software Engineer, Platform

At [Tecton](http://tecton.ai/), 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](https://www.tecton.ai/blog/feature-stores-versus-feature-platforms-feast-tecton/) 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 an early member of Tecton's Multicloud team, you will help build the architecture that supports Tecton across multiple cloud platforms (exs., AWS, GCP, Azure, etc.). You will have an opportunity to directly impact Tecton’s ability to support new customers and use cases that can leverage Tecton to solve their machine learning challenges. With the successful execution of this plan, the MultiCloud team can be responsible for adding directly to our customer base and helping those customers get great value out of Tecton.

### **Responsibilities**

- Write code to implement Tecton capabilities using cloud-provider-native infra. You will become familiar with Tecton-infrastructure pieces and find the best way to integrate and implement them in different cloud environments.
- Own a project or component long-term. Build new capabilities from Development to General Availability, you will be responsible for driving a scalable/maintainable testing and support process, as well as ensuring successful customer adoption.
- Design Tecton software architecture abstractions to speed up the process of onboarding the next new cloud integrations
- Work with other engineering teams to identify the right opportunities to bring projects cross-cloud

### **Qualifications**

- 5+ years of professional backend software engineering experience
- Experience with Terraform, Kubernetes, Python, Kotlin, or Go
- Experience with at least one of AWS, GCP, or Azure
- Experience with Spark, Data Warehouses, and Streaming is a bonus

Software Engineer, Multicloud

At [Tecton](http://tecton.ai/), 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](https://www.tecton.ai/blog/feature-stores-versus-feature-platforms-feast-tecton/) 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.

We are building **[Rift](https://www.tecton.ai/blog/unlocking-real-time-ai-for-everyone-with-tecton/)** - a new fully managed compute environment that allows data scientists to construct powerful batch and streaming feature pipelines in Python. This differs radically from technologies like Spark, which have a steep learning curve and significant overhead to manage, operate, and debug. Our new environment leverages popular open-source technologies such as Ray, Arrow, and DuckDB.

### **As a staff-level engineer building Tecton’s managed compute, you’ll play a critical role in architecting, designing, and scaling our first-ever managed compute platform that will serve as a compute engine used by every Tecton customer. As part of this team, you will be working in one or more of the following areas to build the next generation of Tecton infrastructure:**

- Distributed compute and resource management
- Query optimization and distributed execution
- Cross-platform integrations with state-of-the-art data platforms such as Snowflake and BigQuery
- Fault-tolerant workloads on a lambda-based architecture
- Data security

### **Responsibilities**

- Own and lead large technical domains starting from the problem definition and technical requirements along with implementation and maintenance
- Lead multi-engineer projects of strategic importance to Tecton spanning cross-functional teams including design, product management, and other engineering teams
- Drive efforts to improve engineering practices, tooling, and processes along with mentorship for senior engineers
- Develop a deep understanding of the fundamental problems our customers face in building ML systems
- Be a generalist as needed. We’re a small, but growing engineering team and each engineer needs to be versatile
- Reinforce Tecton’s [“Fast, but Focused”](https://www.tecton.ai/blog/evolving-culture-and-values/) core value

### **Qualifications and Values**

- 7+ years of experience in building product software systems
- 2+ years of technical leadership experience for a group of engineers
- Experience working in large Python, Java, Kotlin, or Go codebases and running cloud-native production systems using Kubernetes, AWS, and Docker
- Experience with distributed systems, SQL, and NoSQL databases
- Bias to action and passion for delivering high-quality solutions
- Strong communication and ability to write detailed technical specifications
- Excitement about coaching and mentorship of junior engineers
- BSc, MS or PhD in Computer Science or related fields

Software Engineer, Compute

At [Tecton](http://tecton.ai/), 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](https://www.tecton.ai/blog/feature-stores-versus-feature-platforms-feast-tecton/) 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 Collaboration team is about focusing on the needs of larger enterprise customers and enabling them to leverage the full potential of a Feature Platform. We make it easy for enterprise customers to manage, administer, and collaborate in the Tecton environment while meeting their security, compliance, and budgetary needs.

### **Responsibilities**

- Primary ownership of building Tecton’s identity management, access control, and data governance that protect the most sensitive data of our customers
- Building out new capabilities for organizing, governing, and sharing features, audit logging, consumption measurement, and usage analytics
- Partnering with other teams to integrate the above with other Tecton capabilities

### **Qualifications**

- An engineer with 5+ years of backend experience (ex., Kotlin, Java, Python, Go, etc.) in Software Engineering
- Passion for building delightful products and end-user UX
- Excellent communication skills
- Preferred: Data infrastructure (Kubernetes, Terraform, etc.) and cloud (AWS, GCP, Azure) experience
- Preferred: Internal analytics experience
- Preferred: Auth and Access control systems experience
- Preferred: B2B SaaS and enterprise features experience
- Preferred: Full stack experience

Software Engineer, Application Security

At [Tecton](http://tecton.ai/), 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](https://www.tecton.ai/blog/feature-stores-versus-feature-platforms-feast-tecton/) 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.

Software Engineer, Realtime Execution

At [Tecton](http://tecton.ai/), 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](https://www.tecton.ai/blog/feature-stores-versus-feature-platforms-feast-tecton/) 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 an Engineering Manager for our Feature Engineering team, you will play a critical role in shaping the team’s strategic direction and implementation of Tecton’s core Feature Engineering SDK, APIs, and developer toolkit. Feature Engineering owns the abstractions and APIs used to build sophisticated ML pipelines used by thousands of developers. The team effectively owns the “hot path UX” through Tecton. Your leadership and technical expertise will directly influence Tecton’s core user experience from sign-up through feature development and finally production. You’ll partner with Product, Design, and Marketing, and Field teams to define the roadmap that helps companies ranging from Fortune 100s to startups accelerate their path to real-time AI.

### **Responsibilities**

- Developer-centric APIs, Python SDKs, and CLIs
- APIs and workflows to support common real-time ML use cases, such as RecSys and Fraud Detection
- Interoperability between Tecton’s logical compute definitions and physical compute integrations, such as Rift
- Core user workflows to support that path from feature development, and iterative model training, to production feature serving

### **Key Responsibilities**

- Leading, and coaching a talented engineering team ranging from junior to staff-level engineers
- Building product and infrastructure for Tecton’s Feature Engineering frameworks and enabling customer adoption of new features
- Recruiting top-tier engineering talent, building an organized and sustainable team, and partnering with internal and external stakeholders
- Partnering with cross-functional teams to build product vision and strategy
- Efficiently executing on an aggressive product roadmap by building new features and managing technical debt levels

### **Qualifications**

- 7+ years of software engineering experience for high-scale products or infrastructure
- 3+ years of experience working on ML engineering or developer-centric platforms
- 2+ years of people management experience for a group of engineers
- Passion for Product and building intuitive, high-quality developer tooling, APIs, and DSLs
- Experience with Batch and Streaming Data Pipelines
- Strong communication and ability to write detailed technical specifications
- Excitement about coaching and mentorship of engineers
- (Nice-to-have) Experience working on large Python projects
- (Nice-to-have) Experience working with versioned APIs and client backward compatibility

Engineering Manager, Feature Engineering

At [Tecton](http://tecton.ai/), 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](https://www.tecton.ai/blog/feature-stores-versus-feature-platforms-feast-tecton/) 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 an Engineering Manager on our Compute team, you will play a critical role in shaping the team’s strategic direction and implementation of Tecton’s core compute foundation, which includes our native compute engine, [Rift](https://www.tecton.ai/blog/unlocking-real-time-ai-for-everyone-with-tecton/), and deep integrations with platforms such as Spark and Snowflake. Think of it as the “beating heart” of Tecton. Your leadership and technical expertise will directly influence Tecton’s storage and compute architecture. This role is a unique opportunity that combines customer-obsessed product focus with platform and data engineering innovation. You’ll partner with Product, Design, and Marketing, and Field teams to define the roadmap that helps companies ranging from Fortune 100s to startups accelerate their path to real-time AI.

### **Responsibilities**

- Building and scaling Tecton’s [Rift](https://www.tecton.ai/blog/unlocking-real-time-ai-for-everyone-with-tecton/) compute engine across multiple cloud providers
- Deep Integrations with Spark (e.g. Databricks, EMR, Dataproc) and Data Warehouses (e.g. Snowflake, BigQuery and Redshift)
- Performant training data computation pipelines from offline stores
- Orchestration platform responsible for reliably and performantly managing compute workloads
- Data engineering for key-value stores and data lakes

### **Key Responsibilities**

- Leading, and coaching a talented engineering team ranging from junior to staff-level engineers
- Building product and infrastructure for Tecton’s Compute platform and enabling customer adoption of new features
- Recruiting top-tier engineering talent, building an organized and sustainable team, and partnering with internal and external stakeholders
- Partnering with cross-functional teams to build product vision and strategy
- Efficiently executing on an aggressive product roadmap by building new features and managing technical debt levels

### **Qualifications**

- 7+ years of software engineering experience for high-scale products or infrastructure
- 2+ years of people management experience for a group of engineers
- Experience working in large Python, Java, Kotlin, or Go codebases and running cloud-native production systems using Kubernetes, AWS, and Docker
- Product and user-first mindset with attention to UX detail
- Experience with distributed systems, SQL, and NoSQL databases
- Bias to action and passion for delivering high-quality solutions
- Strong communication and ability to write detailed technical specifications
- Excitement about coaching and mentorship of junior engineers

Engineering Manager, Compute

At [Tecton](http://tecton.ai/), 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](https://www.tecton.ai/blog/feature-stores-versus-feature-platforms-feast-tecton/) 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 Infrastructure Engineering DevOps team, you will contribute to and own the foundation for building, automating, and scaling Tecton. You will leverage your experience with cloud architectures, distributed systems, containerization technologies (Kubernetes), and Linux system internals to design, build, and maintain our multi-cloud deployments, ensure our systems are secure in-depth, and work closely with the rest of Tecton’s Infrastructure Engineering team to scale and optimize our core compute and online serving systems.

Prior experience with machine learning is not required. We are looking for exceptional DevOps, infrastructure, and software engineers who are driven to find simple solutions to complex challenges. You'll be at the intersection of design, engineering, and operational processes.

### **Responsibilities**

- Own the complete lifecycle of Tecton’s cloud infrastructure development from design through automation, deployment, and operation
- Engage with other engineering and solutions teams to build tools that will accelerate engineering and deployment efficiency
- Develop and maintain infrastructure and tooling to monitor observability of Tecton health, availability, latency
- Joint ownership building and managing Tecton’s CI/CD system to reliably deploy production components with a GitOps model; Including the multi-language, multi-platform Build System based on Bazel
- Participate in an on-call rotation, triaging and addressing Tecton platform major incidents

### **Qualifications**

- Engineer with 5+ years experience in DevOps, SRE, or Software Engineering
- Experience with infrastructure-as-code tools such as Terraform
- Fluent in one or more programming languages such as Python or Golang
- Expertise in cloud providers such as AWS, Google Cloud, and/or Microsoft Azure
- Experience building and troubleshooting robust and secure networks
- Experience with microservices & container orchestration such as Kubernetes
- Expertise in observability stack (Prometheus, ELK, Chronosphere, Datadog, etc.)
- A passion for excellence and high developer productivity
- Strong and effective verbal and written communication skills
- In-depth experience with Linux systems administration and troubleshooting

### **Nice to have**

- Experience building reliable CI/CD pipelines (Github, CircleCI, Buildkite, etc.)
- Experience with Kubernetes configuration management tools (Helm, Kustomize, etc.)
- Experience with GitOps tools (Flux CD, Argo CD)
- Experience with on-call rotation and support of production environments
- Experience working with complex Build Systems (Bazel in particular)
- Experience working with large scale data infrastructure or batch/streaming data pipelines

DevOps Engineer

Tecton

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.

Tecton

Open roles