SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
AlphaSense is seeking an experienced Staff Engineer to lead the design, implementation, and operation of AI-powered systems at scale. The company provides market intelligence and search capabilities built on proven AI, serving over 6,000 enterprise customers including a majority of the S&P 500. Following the 2024 acquisition of Tegus, AlphaSense is accelerating innovation in AI-driven market intelligence.
In this role, you will architect distributed systems that power AI agents processing thousands of requests per hour, ensuring reliability, performance, and cost-efficiency. You'll establish comprehensive testing strategies, observability systems, and CI/CD pipelines that catch issues before customers experience them. You'll mentor a team of talented engineers, sharing expertise in building production systems that maintain high availability. You'll own the technical roadmap for the AI platform, making architectural decisions that shape systems for years to come. You'll collaborate closely with ML engineers and researchers to productionize cutting-edge AI capabilities while maintaining system stability.
Within your first year, you'll transform how the company builds and operates software by reducing incidents, establishing testing and observability standards, and building platforms that enable other engineers to ship faster and safer. You'll elevate the entire engineering team through mentorship and knowledge sharing.
REQUIREMENTS:
Required:
- 8+ 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 engineering cultures
- Experience with multi-model AI systems (OpenAI, Anthropic, Google, etc.)
