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AI Scientist
| Company | Uniphore |
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| Location | Palo Alto, CA, USA |
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| Salary | $154400 – $212300 |
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| Type | Full-Time |
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| Degrees | Master’s, PhD |
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| Experience Level | Mid Level, Senior |
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Requirements
- Ph.D. plus minimum of 2 years of relevant experience; or Master’s degree in Computer Science, Engineering, or related plus a minimum of 3 years of experience in successful delivery of AI services for critical projects
- Minimum of 2 years of industry experience in any of: ML/NLP, Computer Vision, Deep Learning, or ASR
- Thorough knowledge of ML and data science concepts, Deep Learning, PyTorch/TensorFlow
- Hands-on experience in Transformers and Hugging Face ecosystem; hands-on knowledge of using LLMs for NLP
- Solid expertise in Python programming; hands-on experience in developing production-ready, scalable code
- Knowledge of deployment using environments such as: Docker or Kubernetes
- Excellent communication, critical thinking and analytical skills
- Publications in peer-reviewed machine learning venues (e.g. NeurIPS, ICLR, ICML, InterSpeech, ICASSP, EMNLP, KDD, CVPR, AAAI etc.)
Responsibilities
- Collaborate across teams to develop innovative and cross-disciplinary solutions that infuse ultramodern large language model (LLM) technology within Uniphore products
- Leverage the most current tech stacks to develop comprehensive, scalable solutions
- Assemble and fine-tune pre-trained enterprise-grade ML/NLP algorithms
- Integrate ML/NLP with other tools, harnessing of diverse data types
- Align and fine tune ML/NLP/ASR to meet customer needs based on enterprise customer feedback on Uniphore products, enumerate guidelines and design annotation processes for data, working closely with our Data team on data preprocessing, consumption, and automated/manual annotations
- Carry out scientific evaluation of NLP/LLM models; conceive new techniques for model validation, evaluation, trust, and safety
- Manage large datasets, address quality issues, and interpret data according to business goals; compare results of alternative approaches
- Navigate complex metrics and evaluate performance by establishing baseline results from simple or established approaches
- Steer several medium-sized research projects
- Write concise, readable, modular code that requires minimal revision when moved to production
- Write clear documentation of the work plan, its evolution, and results
- Research and assess the scientific literature for solutions to company needs, and compare prior work to internal solutions
Preferred Qualifications
No preferred qualifications provided.
