You are a fast learner, with an eye for detail, strong problem-solving and debugging skills, and a passion for applying AI to complex engineering problems. In addition you have the following qualifications:
- Bachelor’s, Master’s degree or PHD in Computer Science, AI/ML, Mathematics, or a related quantitative field.
- 7+ years of experience, including demonstrated technical leadership. Ph.D. holders may have 6+ years experience.
- Proven track record of leading the design and delivery of production ML systems end to end, and setting technical direction that other engineers build on.
- Deep expertise in representation learning and embeddings, vector similarity search, and retrieval-augmented approaches able to architect the foundation-model core, not just contribute to it.
- Strong command of ML fundamentals, including regression, classification, forecasting, and model interpretability.
- Hands-on experience with deep learning architectures such as CNN, LSTM, GRU, GANs, BERT, Diffusion Models, and Transformers.
- Substantial experience with LLMs (e.g., GPT-3/4, Claude, Mistral) and architecting agent-based solutions using frameworks like LangGraph, CrewAI, or AutoGen.
- Multimodal / vision-language model experience jointly encoding imagery with text or structured data at an architectural level.
- Proficiency in Python; familiarity with other programming languages like C++ or Java is a plus.
- Fluency with ML frameworks such as PyTorch, TensorFlow, scikit-learn, and experience working with large datasets and distributed systems.
- A track record of making independent architectural decisions, defending them with evidence, and holding a high measurement bar for whether systems actually work.
- Demonstrated ability to mentor senior engineers and lead cross-functional, agile teams.