Cybersecurity startup using machine learning to tame vulnerability noise for security teams
Core AI/ML Software Engineer
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You'll spend mornings pairing with threat researchers to understand which CVEs actually matter, then translate that intuition into models that score and sort vulnerabilities by business risk. Afternoons might mean prototyping a new prioritization algorithm in PyTorch, debugging a Go service feeding your vector database, or building an Airflow pipeline that stitches together exploit-intel feeds from a dozen sources. The team ships continuously—your PRs land the same day, and you'll see your models change how Fortune 500 security teams spend their Tuesday afternoons. This is hands-on work: you own the full lifecycle from notebook experiment to production API, not just a Jupyter cell someone else deploys.
What they're looking for
- 8+ years shipping production ML systems, with at least 3 years in security, fraud, or similarly adversarial domains
- Fluency across Python and one systems language—Go or Rust preferred—for building services that don't fall over at 3 AM
- Demonstrated experience with retrieval-augmented generation, embeddings, or other LLM application patterns beyond basic API calls
- Comfort owning models end-to-end: training infrastructure in MLflow or equivalent, observability, drift detection, and rollback decisions
- Strong software engineering fundamentals—version control, testing, CI/CD, and the judgment to trade research elegance for operational simplicity