Senior Staff Engineer, AI
AlphaSense is seeking a visionary and hands-on Senior Staff AI Engineer to lead the development of AI solutions within our Content portfolio. This role offers the opportunity to shape the future of AI at AlphaSense by researching, architecting, and implementing systems that process and extract insights from millions of unstructured documents and multimedia files daily. As the primary AI/ML authority, you will be instrumental in advancing our product offerings and establishing a center of excellence in AI.
In this position, you will spearhead AI innovation by evaluating and prototyping cutting-edge solutions using Large Language Models (LLMs), Computer Vision, and other machine learning techniques to address complex data extraction challenges. You will design and build robust, scalable, and cost-effective AI services and data processing pipelines capable of handling millions of documents daily with high reliability and throughput. Additionally, you will tackle real-world AI challenges, such as managing LLM latency and variance, developing sophisticated prompt engineering strategies, and building fault-tolerant systems that perform consistently.
The ideal candidate will have over 10 years of professional software engineering experience, with a proven track record in building complex, data-intensive backend systems. Deep expertise in building and scaling production-grade services using modern backend frameworks such as FastAPI, Django, Sanic, or Spring Boot is essential. Significant hands-on experience in the complete lifecycle of AI/ML models—from experimentation and prototyping to deployment, monitoring, and iteration in a high-volume cloud environment—is required. Mastery in designing large-scale distributed systems, with strong knowledge of asynchronous patterns, streaming/queuing/caching strategies, and robust observability, is also crucial. Exceptional communication and leadership skills are necessary to articulate complex technical concepts and influence engineering direction across multiple teams without direct authority.
Proficiency with modern DevOps and MLOps practices, including CI/CD pipelines, Infrastructure as Code (IaC), and automated testing frameworks, is highly desirable. Hands-on experience with containerization and orchestration technologies, particularly Docker and Kubernetes, is also beneficial.