Applied AI Engineer

Role overview Turn machine learning capabilities into useful, dependable product behavior. You’ll own AI features across the model, supporting systems, and user experience, with a focus on making them work consistently in production rather than only in demos.

Responsibilities - Build and ship AI features from model behavior through system integration and user-facing outcomes. - Design and refine prompts, tools, memory, and agent workflows. - Structure model outputs into predictable, reliable behavior. - Investigate issues across models, orchestration, infrastructure, and UX. - Improve latency, cost, and reliability of production features. - Create lightweight evaluations, use real-world signals to guide iteration, and collaborate with product and engineering to turn unclear problems into working systems.

Requirements - Strong grounding in machine learning and modern neural network architectures. - Practical experience training, fine-tuning, or deploying ML models. - Ability to write clean, production-quality code. - Comfort working across model, infrastructure, and product layers. - Strong problem-solving skills in ambiguous, fast-changing work. - A bias toward shipping, iteration, and continuous improvement.

Nice to have Experience with Python, PyTorch or JAX, large language models, model serving such as vLLM, and vector databases is relevant to the stated technical environment.

Benefits and work setup The team describes a small, hands-on environment with collective decision-making, rapid iteration, and independent ownership. The interview process typically involves three or four conversations, held virtually and/or onsite.