Machine Learning Engineer

September 20, 2024

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Valence

Valence is a company that offers AI-powered leadership coaching solutions. Their flagship product, Nadia, is an AI coach that provides personalized, expert, and human-like guidance to leaders and employees. The AI coach is designed to enhance leadership skills, team cohesion, and communication. Valence focuses on leveraging scientifically proven coaching methods and integrating them with internal values and leadership frameworks. They emphasize privacy, security, and compliance, making their solutions enterprise-ready. Valence is aimed at scaling talent development by providing 1:1 coaching opportunities across organizations.

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

πŸ“‹ 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.

πŸ–οΈ 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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September 7, 2024

Lead development of machine learning models for commercial insurance risk assessment.

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