Senior Software Engineer, Investigations - SpyCloud

SpyCloud is on a mission to make the internet a safer place by disrupting the criminal underground. SpyCloud’s solutions thwart cyberattacks and protect more than 4 billion accounts worldwide. Cybersecurity is an exciting, evolving space, and being at the forefront of the fight to disrupt cybercrime makes SpyCloud a special place to work. If you’re driven to align your career with a fantastic mission, look no further!

We're looking for a Senior Software Engineer to join the team behind Investigations, the part of the SpyCloud Console that analysts use to run down a lead. They start with a single identifier, usually an email address or a username, and we resolve it into the other personas behind it and the breach and malware records those personas appear in. The corpus is billions of recaptured records, and analysts expect an answer in seconds.

You'll work across the whole module: a Go API, a React front end built around an entity-relationship graph, and a Python agent that does the pivoting when an analyst asks a question in plain language. You'll work directly with a product lead and a designer, and the agentic side of the product is new enough that you'll help decide what it becomes.

Our Stack

• Front End: React, Vite, TypeScript
• Back End: Golang, REST APIs
• AI: Python, AWS Bedrock, LangGraph
• Frameworks: Vite, MaterialUI, Gin
• Data: PostgreSQL, Databricks
• Infrastructure: AWS, Docker, Terraform

What You'll Do

1. Feature Development

• Take work from ambiguous business intent through to a shipped, operated service, directing AI agents through planning, implementation, and test generation, and staying accountable for the result.
• Own production outcomes for Investigations: reliability, performance, and cost.
• Troubleshoot customer-reported issues, including the ones that turn out to be a query plan change.

2. Technical Leadership

• Make and defend design decisions on a module you will know better than anyone outside the team.
• Set technical direction for Investigations, including the specs, context, and guardrails that decide whether agent-generated work is any good.
• Own verification when generation is cheap. A plan, its implementation, and its tests can all be drafted in an afternoon, so the review gates that keep customer data trustworthy are a design problem, and yours to solve.
• Raise the bar on testing, observability, and the interfaces between our module and the Console platform it deploys into.

3. Team Collaboration and Improvement

• Adopt existing team practices and recommend improvements as needed.
• Mentor engineers on the team, including how to work with agents effectively, and help build a culture of continuous learning.
• Improve how the team builds, including the AI tooling and evaluation harnesses we use to move faster without shipping regressions.

Requirements

• Professional Experience: At least 5 years delivering production software, including work other teams depended on, and the judgment to know a design is wrong before it's expensive.
• Technical Proficiency: Depth in Go or TypeScript, and quick to pick up the other. Designs, versions, and evolves RESTful APIs other teams depend on. Fluent with PostgreSQL and large analytical datasets with strong fundamentals in data structures, algorithms, and system design.
• Cloud Experience: Production experience with AWS Lambda, API Gateway, ECS, and EC2.
• Communication: Sharp writing. Specs and design docs are the primary artifact, for humans and agents both.
• Engineering Practices: Full lifecycle fluency: code review, source control, build and deploy (GitHub, GitHub Actions, AWS CodeBuild). Owns testing, CI/CD, observability, and on-call for what you ship.
• How You Work: Ships production code with agentic tools, decomposes ambiguous problems into work an agent can execute, reads unfamiliar code fast, and digs into unfamiliar parts of a system rather than routing around them.

Nice to Have

• Built an internal tool or agent workflow that other engineers adopted.
• Built evals, golden datasets, or regression checks for LLM output.
• Adopted an AI-native delivery method such as spec-driven development or AWS's AI-DLC.
• Large-scale data processing with Spark or Databricks.
• Python for agent work, such as LangGraph or Bedrock.
• Background in cybersecurity or identity threat protection.