Senior Software Engineer (Investigations)

• 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

• 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

• 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

• 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

• 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

Benefits

• Health benefits: From medical to dental, we’ve got you covered.

• Work/life balance: We offer generous PTO and a remote-friendly culture.

• Compensation: Our talented employees enjoy competitive salaries and equity.

• 401k matching: Investing in the future of our employees is a no-brainer.

• SpyCares: We’re only as strong as the communities we’re a part of and giving back is in our DNA.- Fluent with PostgreSQL and with large analytical datasets

• Technical Proficiency:

• Communication:

• 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

• Depth in Go or TypeScript, and quick to pick up the other

• Ships production code with agentic tools, and can say where they failed and what changed

• Full lifecycle fluency: code review, source control, build and deploy. We use GitHub, GitHub Actions, and AWS CodeBuild

• Reads unfamiliar code fast and catches the plausible-but-wrong. Treats AI output as a draft, not an answer

• Designs, versions, and evolves RESTful APIs other teams depend on

• Owns testing, CI/CD, observability, and on-call for what you ship

• How You Work:

• Learns deliberately, and can point to something picked up recently

• Decomposes ambiguous problems into work an agent can execute. The spec sets the ceiling

• Digs into unfamiliar parts of a system rather than routing around them

• Engineering Practices:

• Production experience with AWS Lambda, API Gateway, ECS, and EC2

• Sharp writing. Specs and design docs are the primary artifact, for humans and agents both

• Strong fundamentals in data structures, algorithms, and system design

• Cloud Experience:

• Built an internal tool or agent workflow that other engineers adopted

• Built evals, golden datasets, or regression checks for LLM output, whether a product feature or code generation

• Adopted an AI-native delivery method such as spec-driven development or AWS’s AI-DLC, including the agent rules that make it repeatable

• Large-scale data processing with Spark or Databricks

• Python for agent work, such as LangGraph or Bedrock

• Background in cybersecurity or identity threat protection