Staff Engineer, Software

Role details

About AlphaSense: The world’s most sophisticated companies rely on AlphaSense to remove uncertainty from decision-making. With market intelligence and search built on proven AI, AlphaSense delivers insights that matter from content you can trust. Our universe of public and private content includes equity research, company filings, event transcripts, expert calls, news, trade journals, and clients’ own research content. The acquisition of Tegus by AlphaSense in 2024 advances our shared mission to empower professionals to make smarter decisions through AI-driven market intelligence. Together, AlphaSense and Tegus will accelerate growth, innovation, and content expansion, with complementary product and content capabilities that enable users to unearth even more comprehensive insights from thousands of content sets. Our platform is trusted by over 6,000 enterprise customers, including a majority of the S&P 500. Founded in 2011, AlphaSense is headquartered in New York City with more than 2,000 employees across the globe and offices in the U.S., U.K., Finland, India, Singapore, Canada, and Ireland. Come join us! 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 AlphaSense is an equal-opportunity employer. We are committed to a work environment that supports, inspires, and respects all individuals. All employees share in the responsibility for fulfilling AlphaSense’s commitment to equal employment opportunity. AlphaSense does not discriminate against any employee or applicant on the basis of race, color, sex (including pregnancy), national origin, age, religion, marital status, sexual orientation, gender identity, gender expression, military or veteran status, disability, or any other no