Machine Learning Engineer

September 20

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Valence

Team Development β€’ SaaS β€’ Human Resources β€’ Team Performance β€’ Manager enablement

Description

β€’ Architect and develop enterprise-grade conversational AI solutions for leadership coaching. β€’ Develop, design and implement improvements in user experience in conversational interactions leveraging LLMs in novel ways to advance product goals. β€’ Evaluate and improve existing conversational (LLM-based) models across dimensions of effectiveness, scalability, and efficiency. β€’ Implement, test, and deploy LLM-powered coaching agents that understand complex tasks, provide accurate and relevant responses, and adapt to diverse conversational contexts. β€’ Integrate and manage diverse data sources to enhance the knowledge and contextual understanding of our AI coaching models. β€’ Work with the product team to study user behavior and prioritize evolving product developments. β€’ Experiment at a high velocity to optimize user experience. β€’ Full stack - write, review and deploy code across back and front end as needed. β€’ Streamlining data science processes to support rapid iteration and quality improvement. β€’ Support other science and software development where required.

Requirements

β€’ Bachelor's degree in Computer Science, Engineering, Mathematics, related field, or equivalent experience. β€’ 3+ years of professional experience (or equivalent) in software engineering, AI/ML development (ideally including a Master's or Ph.D. in Computer Science, ML, Data Science, or a related field). β€’ Practical and theoretical knowledge of language systems in the areas of: conversational systems, NLP, and Information Retrieval with knowledge of relevant tools. β€’ Strong software engineering skills with a track record of developing data-driven machine learning systems or products. β€’ Proficiency in Python and relevant deep learning frameworks - both training (e.g. PyTorch, Tensorflow, JAX) and serving (e.g., Hugging Face TGI/Transformers/Adapters/outlines, vLLM). β€’ Experience with cloud deployment of ML systems (e.g., AWS, GCP, Azure) including and open systems (e.g. Docker and Kubernetes) and their associated ML services. β€’ Experience with Data Science tools and processes (e.g. NumPy, scikit-learn, Pandas, PySpark). β€’ Familiarity with ML lifecycle tools like MLflow, Weights & Biases. β€’ Hands-on experience building Generative AI-powered applications, including Large Language Models. β€’ Strong analytical and problem-solving skills. β€’ Ability to communicate complex ideas and concepts effectively. β€’ Exposure to early-stage startups, preferably B2B SaaS.

Benefits

β€’ Ownership of projects and strategic priorities regardless of seniority in our learning-focused environment. β€’ Strong ties to the executive team, a culture of transparency and engagement with strategic decisions. β€’ Options from day one, which means you will be on the ownership track right away. β€’ Competitive salary and equity packages. β€’ Comprehensive health coverage (medical, dental, and vision) from day 1. β€’ Provision of anything you need to be successful - learning tools, hardware, office equipment, software. β€’ 401k optionality for US based employees. β€’ Generous PTO, company-wide R&R shutdowns and paid leave for parents. β€’ A WFH stipend, phone stipend and support to work in a We Work or other space as preferred.

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