Product Operations partners with a variety of different engineering and operations teams, our team leads development of machine learning solutions. We deliver projects from end-to-end: problem statement and conceptualization, proof-of-concept, and participation in final deployment! You will also perform ad-hoc statistical analyses. You will also work closely with data engineers to generate detailed business intelligence solutions. You will be expected to conduct presentations of analyses to a wide range of audiences including executives. Product Operations partners with a variety of different engineering and operations teams, our team leads development of machine learning solutions. We deliver projects from end-to-end: problem statement and conceptualization, proof-of-concept, and participation in final deployment!
You will also perform ad-hoc statistical analyses. You will also work closely with data engineers to generate detailed business intelligence solutions. You will be expected to conduct presentations of analyses to a wide range of audiences including executives.3+ years experience in GenAI applications, machine learning algorithms, software engineering, and data mining models with an emphasis on large language models (LLM) or large multimodal models (LMM). Masters in Artificial intelligence, Machine Learning, Computer Science, Statistics, Operations Research, Physics, Mechanical Engineering, Electrical Engineering or related field.Proven experience in GenAI application building with agents and agentic workflows. Experience with LLM and LMM development and fine-tuning is a major plus. Proficiency in using cutting-edge GenAI tools, i.e. Claude Code, Roo Code, etc. Familiarity with distributed computing, cloud infrastructure, and orchestration tools, such as Kubernetes, Apache Airflow (DAG), Docker, Conductor, Ray for LLM training and inference at scale is a plus. Hands-on experience with LangChain and LlamaIndex, enabling RAG applications and LLM orchestration. Ability to meaningfully present results of analyses in a clear and impactful manner, breaking down complex ML/LLM concepts for non-technical audiences. Experience applying ML techniques in manufacturing, testing, or hardware optimization is a major plus. Proven experience in leading and mentoring teams is a plus.
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