Machine Learning Engineer

September 20

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Team Development • SaaS • Human Resources • Team Performance • Manager enablement

11 - 50

Description

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

Requirements

• Bachelor's degree in Computer Science, Engineering, Mathematics, related field, or equivalent experience. • 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). • Practical and theoretical knowledge of language systems in the areas of: conversational systems, NLP, and Information Retrieval with knowledge of relevant tools. • Strong software engineering skills with a track record of developing data-driven machine learning systems or products. • 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). • 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. • Experience with Data Science tools and processes (e.g. NumPy, scikit-learn, Pandas, PySpark). • Familiarity with ML lifecycle tools like MLflow, Weights & Biases. • Hands-on experience building Generative AI-powered applications, including Large Language Models. • Strong analytical and problem-solving skills. • Ability to communicate complex ideas and concepts effectively. • Exposure to early-stage startups, preferably B2B SaaS.

Benefits

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