AI Research Engineer II
Work Model: Hybrid
AlphaSense is a market intelligence and search platform used by the world's leading companies and financial institutions. Since 2011, our AI-based technology has helped professionals make smarter business decisions by delivering insights from an extensive universe of public and private content—including equity research, company filings, event transcripts, expert calls, news, trade journals, and clients’ own research content.
Headquartered in New York City, AlphaSense employs over 1,500+ people across offices in the U.S., U.K., Finland, Germany, India, and Singapore.
For more information, please visit www.alpha-sense.com
Check out what we’ve built so far:
1. The decision that matters
2. India Office -
About the Role:
We are seeking a passionate research engineer to join our AI Research team. The AI Research team develops the cutting edge deep learning, NLP, search, and generative AI algorithms that power AlphaSense Search and AI products. You'll work closely with a team of talented researchers and engineers to deliver scalable solutions for our world class AI powered search platform.
- 5-7 years of experience would be ideal for this position
- An expert in machine learning and deep learning. Ideally, you possess an advanced degree in Computer Science, Computer Engineering or relevant field with a focus on machine learning, but equivalent industry experience may be considered
- Familiar with the state-of-the-art deep learning and natural language processing research
- Extremely proficient in developing end-to-end NLP models using Python and NLP libraries like Spacy and HuggingFace
- Familiar with deep learning frameworks like PyTorch and TensorFlow
- An excellent communicator with strong organizational, problem-solving, debugging and analytical skills
- Develop highly scalable deep learning models and take them all the way to production
- Perform cutting edge research in deep learning, machine learning & natural language processing
- Work closely with cross-functional teams to translate product requirements into machine learning architectures
- Own systems end-to-end including design, code, training, testing, deployment and iteration
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