Senior Machine Learning Engineer
FieldguideFull Time
Senior (5 to 8 years)
Candidates should possess a Bachelor's degree in a quantitative discipline or equivalent experience in data-intensive problem-solving, with a Master's or PhD being preferred. A minimum of 5 years of experience in a data-intensive or machine learning role is required, including at least 2 years in an industry setting. Demonstrated experience in training and deploying machine learning models, particularly in NLP applications, is essential. Deep proficiency in the Python data and machine learning ecosystem, including PyTorch, is necessary. Familiarity with Docker, Python API frameworks like FastAPI, and software engineering best practices is expected. A practical, iterative, product-focused mindset is preferred over a slower, methodical, research-minded approach. Previous experience deploying microservices on Kubernetes is a plus.
The Senior AI Engineer will build and release product features powered by machine learning, leveraging proprietary datasets and the latest advances in the field. This includes prototyping, writing production Python code within internal machine learning packages, and deploying production microservices. The role involves staying current with machine learning advancements and sharing knowledge with the team. Additionally, the engineer will influence the architecture and deployment of machine learning code and services, and mentor junior ML engineers. Collaboration with product managers and other engineers is key to developing features on the AlphaSense Platform.
AI-driven financial research platform
AlphaSense provides a financial research platform that uses artificial intelligence to help clients gain insights into various companies, industries, and market trends. Users can search through a vast library of over 300 million documents, which includes both external and proprietary content. The AI technology streamlines the research process by automating repetitive tasks, allowing clients to concentrate on analyzing the information instead. Additionally, AlphaSense monitors real-time data to highlight important insights, helping clients stay ahead in a rapidly changing market. Unlike its competitors, AlphaSense is particularly focused on data security, implementing advanced measures like the zero trust security model and secure data encryption. The goal of AlphaSense is to empower clients in making informed decisions, whether they are launching new products or providing strategic insights to executives.