Staff AI Platform Engineer
AlphaSenseSan Francisco, California, United StatesPosted 26 March 2026
Tech Stack
Job Description
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 SP 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 Team:
Our diverse Product Engineering team values innovation, collaboration, and the continuous improvement of our platform. We offer a dynamic work environment where employees can grow and thrive while working on cutting-edge technology that shapes the future of AI and search.
At AlphaSense, we believe that diverse experiences and perspectives are critical to our success. We are committed to creating an inclusive workplace where all employees feel valued and empowered to be their authentic selves.
About the Role
AlphaSense is seeking an experienced engineering leader to transform how we build and operate AI-powered systems at scale. You'll join a team of brilliant engineers who've built cutting-edge AI applications, and your mission will be to bring world-class engineering practices that ensure these innovations run reliably for our enterprise customers.
This is a unique opportunity for a seasoned engineer who thrives on building robust platforms, mentoring talented teams, and establishing engineering excellence. You'll have significant autonomy to shape our technical architecture while working with frontier AI models and agentic systems that power market intelligence for the world's leading companies.
What You'll Do
Architect for Scale : Design and implement distributed systems that power AI agents processing thousands of requests per hour, ensuring reliability, performance, and cost-efficiency
Build Engineering Excellence : Establish comprehensive testing strategies, observability systems, and CI/CD pipelines that catch issues before customers do
Lead Through Expertise : Mentor a team of smart, motivated engineers, sharing your experience in building production systems that don't break at 3 AM
Drive Platform Evolution : Own the technical roadmap for our AI platform, making architectural decisions that will shape our systems for years to come
Bridge AI and Engineering : Collaborate with ML engineers and researchers to productionize cutting-edge AI capabilities while maintaining system stability
What We're Looking For
Required:
7+ years building and operating distributed systems in production
Track record of improving system reliability (taking services from frequent outages to 99.9%+ uptime)
Deep expertise in modern engineering practices: microservices, containerization (Kubernetes), infrastructure as code
Experience leading technical initiatives and mentoring engineering teams
Strong coding skills with ability to work across the stack
Excellence in debugging production issues and implementing comprehensive observability
History of making pragmatic trade-offs between perfect and shipped
Preferred:
Experience with LLM applications, agent frameworks, or AI/ML infrastructure
Familiarity with prompt engineering, RAG patterns, vector databases
Background at high-growth companies or modern eng ... (truncated, view full listing at source)
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