Software Engineer

Reposted Yesterday
Santa Clara, CA, USA
In-Office
120K-140K Annually
Junior
Artificial Intelligence • Information Technology • Software
The Role
Build and maintain production-grade Python ETL pipelines, data models, and integrations. Apply agentic AI workflows for automated data engineering and root-cause analysis. Work directly with customers to integrate and deploy the platform, translate data requirements into technical solutions, and document feedback for product improvements.
Summary Generated by Built In

About Us

Selector is building an operational intelligence platform for digital infrastructure. Using an AI/ML-based analytics approach, the platform provides actionable, multi-dimensional insights to network, cloud, and application operators. It helps operations teams meet their KPIs through seamless collaboration, a search-driven conversational experience, and automated data engineering pipelines.

Our solutions are used by leading Telecom, Media, Health Care, Finance, Retail, Professional Sports, and Fortune 500 enterprise organizations around the world. Our novel approach and rapidly expanding footprint position us for continued growth as a category leader.

Perks: discretionary PTO, health insurance, 401k, bonus potential, and more.

About the Role

This is a backend-heavy role for engineers who want to solve complex data and systems problems — and see that work run in production, inside a customer's environment, not just in a sprint backlog. You'll build and maintain the ETL pipelines, data models, and integrations that power Selector's platform, applying agentic AI workflows to help infrastructure reason about itself at scale.

You'll also work directly with customers to understand their data and environment, translating complex technical concepts into solutions that fit their specific, high-stakes infrastructure — so your backend work isn't abstract, it's solving a real problem for a real team.

Responsibilities

  • Design, write, and maintain production grade Python code for ETL pipelines, data models, and platform integrations.
  • Apply agentic AI workflows to automate data engineering, correlation, and root cause analysis across complex infrastructure data.
  • Build backend logic connecting Selector's platform to customer data sources, APIs, and existing tooling.
  • Work directly with customers to understand complex data structures, workflows, and analytics requirements, translating them into technical solutions.
  • Support hands  on integration and deployment of Selector's platform to meet customer requirements, working alongside senior engineers.
  • Help ensure customers are enabled for use and basic self-serve extensions of Selector's capabilities.
  • Document customer feedback and translate it into concrete product and engineering improvement proposals.

You Will Thrive If You

  • Enjoys coding and data analysis — especially ETL, data pipelines, and complex system design — and want to see your work run in front of real customers.
  • Like applying AI/agentic workflows to solve genuinely hard, ambiguous problems, not just well defined ones.
  • Lead by example and remain hands on with testing and automation.
  • Can evaluate whether a system output is truly correct, not just whether a test passed.
  • Balance quality, speed, and pragmatism when building under real customer constraints.
  • Build processes that improve quality without introducing unnecessary overhead.
  • Enjoy solving complex technical problems and working closely with both engineering and customer teams.

Requirements

  • Bachelor's degree or higher in Computer Science, Engineering, or a related field.
  • 0–3 years of professional experience in a technical or engineering role.
  • Solid coding skills in Python, with comfort building ETL pipelines and working with complex data structures; exposure to Ansible or Go is a plus.
  • Basic familiarity with public cloud platforms such as AWS, GCP, or Azure.
  • Exposure to containerization technologies such as Docker and Kubernetes.
  • Working understanding of SQL for data extraction and analysis; familiarity with Jupyter notebooks is a plus.
  • Interest in or exposure to agentic AI workflows and applying them to real-world engineering problems is a plus.
  • Strong communication skills, both verbal and written, with a desire to work in a customer-facing environment.
  • General awareness of networking concepts (e.g., Data Center, WAN, DNS/DHCP) — deeper expertise can be developed on the job.
  • Eagerness to learn, a self-starter attitude, and the ability to thrive in a fast-paced, collaborative environment.

Compensation

The salary for this role is $120,000 – $140,000. Final offer amounts are determined by multiple factors, including prior experience and job location, and may vary from the amount listed.

Skills Required

  • Bachelor's degree or higher in Computer Science, Engineering, or related field.
  • 0-3 years of professional experience in a technical or engineering role.
  • Solid coding skills in Python, with experience building ETL pipelines and working with complex data structures.
  • Exposure to Ansible or Go.
  • Basic familiarity with public cloud platforms such as AWS, GCP, or Azure.
  • Exposure to containerization technologies such as Docker and Kubernetes.
  • Working understanding of SQL for data extraction and analysis.
  • Familiarity with Jupyter notebooks.
  • Interest in or exposure to agentic AI workflows and applying them to engineering problems.
  • Strong verbal and written communication skills and comfort in a customer-facing environment.
  • General awareness of networking concepts (Data Center, WAN, DNS/DHCP).
  • Eagerness to learn, self-starter attitude, and ability to thrive in a fast-paced, collaborative environment.
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The Company
HQ: Santa Clara, CA
104 Employees
Year Founded: 2019

What We Do

Selector AI is the industry leading AIOps platform designed to provide instant, real-time actionable insights for managing multi-domain network and application infrastructures. By bringing together multiple sources of data into one easy to use platform, IT teams can troubleshoot network issues faster, avoid downtime, reduce MTTR and improve efficiency.

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