Staff Engineer, Software

About the Role

We're looking for a Staff Software Engineer who owns technical direction, thrives in ambiguity, and uses AI as a natural part of how they build software. You won't just write code — you'll make decisions that shape systems for years, mentor engineers across teams, and drive the engineering bar higher across the organization.

This team designs and operates large-scale systems that ingest, process, and enrich diverse content like filings, transcripts, news, research, and more. In this role, you will build and own backend services and high-throughput data pipelines that turn raw content into structured, searchable intelligence.

As a Staff Engineer, you'll operate at the intersection of technical depth and organizational influence — turning ambiguous business problems into executable technical strategies, and shipping them end-to-end.

What You'll Do

Set technical direction for your area — make build-vs-buy decisions, define architecture, and own the technical roadmap alongside product leadership.

Take ambiguous problems and make them concrete — scope work, identify risks, break down large initiatives into deliverable increments, and drive alignment across teams.

Design and deliver production-grade systems — scalable pipelines, robust services, and high-performance solutions that serve real users at scale.

Leverage AI tools as part of your workflow — you use AI-assisted development (Claude Code, Cursor, Copilot) to accelerate your work and have formed opinions on when it helps and when it gets in the way.

Evaluate and integrate AI/ML capabilities into production systems when the problem calls for it — you don't need to be an ML researcher, but you're sharp enough to pick up LLMs, embeddings, or classification models and put them to work.

Drive cross-team technical initiatives — influence engineers and teams you don't manage. Lead RFCs, drive architectural reviews, and build consensus on hard technical decisions.

Own what you build — from requirements to release to production. You build it, you run it. You monitor SLOs/SLIs, troubleshoot production issues, and continuously improve reliability.

Raise the engineering bar — through code reviews, mentorship, technical documentation, and by modeling the standards you expect from others.

Must Have

Strong in Python (our primary backend language). Comfortable working across languages — you've shipped production code in at least two.

Designed and owned production systems serving real users at scale — not just contributed to them, but made consequential architectural decisions and lived with the outcomes.

Led cross-team technical initiatives without formal authority — driven migrations, platform changes, or architectural shifts that required aligning multiple teams.

Strong system design instincts — you think in terms of failure modes, data flow, scalability, and operational cost. You design for the system you'll maintain, not just the one you'll ship.

Deep DevOps and operational experience — Kubernetes, cloud infrastructure (AWS/Azure/GCP), CI/CD, observability. You don't throw code over the wall.

Track record of mentoring engineers and raising team standards — through pairing, reviews, RFCs, and leading by example.

Good to Have

Experience leading large-scale migrations or platform rewrites

Hands-on experience with AI/ML in production — LLMs, BERT, NLP pipelines, or document understanding systems

Contributed to or driven engineering-wide standards, practices, or tooling

Experience with content processing, enrichment, or search systems at scale

Familiarity with Java (parts of our stack)

Experience with GitOps, ArgoCD, or Infrastructure as Code

Active practitioner of AI-assisted development (AIDLC) — uses AI tools daily in their engineering workflow

How We'll Evaluate You

Our technical interview includes a hands-on session in a real development environment — not a whiteboard. You'll work on a realistic, messy codebase with AI tools pre-configured and available. We're evaluating how you think, how you use tools, and how you approach problems — not whether you've memorized algorithms.

What we look for:

How you navigate and make sense of unfamiliar code

How you leverage AI tools — critically, not blindly

How you decompose problems and make incremental progress

How you communicate trade-offs and decisions as you work