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You will be updated with latest job alerts via emailThe Senior Applied Scientist will play a key role in the development and evaluation of LLM and vLLM models with a focus on creating innovative solutions to real-world problems. This role requires a deep understanding of machine learning techniques including foundation model evaluation traditional NLP/CV metrics LLMaaJ techniques human evaluation confidence estimation agentive application evaluation and RAG application evaluation. The successful candidate will have experience in conducting in-depth research producing production-ready code and collaborating with cross-functional teams to integrate evaluation capabilities into various applications and products. A strong background in programming languages such as Python and experience with machine learning frameworks such as TensorFlow or PyTorch is also required.
The successful candidate will be responsible for conducting in-depth research on foundation model evaluation producing production-ready code for handoff to engineering counterparts and helping to build and mentor a high-performing team of scientists and engineers. They will work closely with cross-functional teams to integrate evaluation capabilities into various applications and products identify new opportunities for evaluation and explore emerging technologies. The candidate will also be responsible for staying up-to-date with industry trends and advancements in evaluation and for applying this knowledge to drive innovation and improvement in the teams work. This will involve designing and executing experiments researching new algorithms and finding new ways of optimizing risk profitability and customer experience. A PhD in Computer Science Mathematics Statistics Physics Linguistics or a related field with a dissertation or thesis centered on machine learning techniques is preferred although a Masters or Bachelors degree with relevant experience will also be considered.
Career Level - IC4
Responsibilities:
Research and Development: Conduct in-depth research on foundation model evaluation including traditional NLP/CV metrics LLMaaJ techniques human evaluation confidence estimation agentive application evaluation and RAG application evaluation. Produce production-ready code for handoff of POC applications to counterparts in Engineering.
Collaboration: Work closely with cross-functional teams to integrate evaluation capabilities into various applications and products.
Team Leadership: Help lead and mentor a high-performing team of scientists and engineers.
Innovation: Identify new opportunities for evaluation and explore emerging technologies.
Stay Updated: Maintain a deep understanding of industry trends and advancements in evaluation.
Qualifications and Experience:
PhD Computer Science Mathematics Statistics Physics Linguistics or a related field (with a dissertation thesis or final project centered in Machine Learning/Deep Learning/Generative AI/Computational Linguistics) and a minimum of 4 years work experience. Candidates without a PhD but with 5 additional years experience will be considered.
Strong publication record including as a lead author or reviewer in top-tier journals or conferences.
Extensive experience in generative AI and model/application evaluation.
Strong understanding of machine learning algorithms and architectures
Excellent problem-solving and analytical skills
Strong leadership and communication abilities
Required Experience:
Staff IC
Full-Time