Staff Software Engineer

Were 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 wont just write code - youll make decisions that shape systems for years, mentor engineers across teams, and drive the engineering bar higher across the organization.

This role sits within the Unstructured Content Portfolio, Broker Research domain - the systems that acquire, process, and deliver equity research from major brokers, and the authorization layer that controls who can access what. Youll work on real-time ingestion pipelines, multi-vendor API integrations, entitlement enforcement across distributed systems, and content delivery at scale - where compliance, reliability, and low-latency processing matter deeply.

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

• 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 dont need to be an ML researcher, but youre 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 dont 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.

• Strong in Python (our primary backend language). Comfortable working across languages - youve 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 youll maintain, not just the one youll ship.

• Deep DevOps and operational experience - Kubernetes, cloud infrastructure (AWS/Azure/GCP), CI/CD, observability. You dont throw code over the wall.

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

• 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

• Real impact at scale - our entitlements engine manages complex workflows for an exponentially growing number of entitlement data for users and documents. Your architectural decisions will directly affect Top Global Companies.

• Autonomy with accountability - Staff Engineers at AlphaSense own their technical area. Youll have the freedom to make big decisions and the responsibility to make them stick.

• Hard problems, not busywork - Complex rule engine, Multi-format document processing, real-time entitlements with low latency. The problems are genuinely interesting.