Applied AI Engineer
| Company | Valence |
|---|---|
| Location | Toronto, ON, Canada, New York, NY, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Bachelor’s |
| Experience Level | Mid Level, Senior |
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
Responsibilities
- 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.
Preferred Qualifications
- Exposure to early-stage startups, preferably B2B SaaS