Applied AI Engineer

Role overview Build AI features that work reliably for people in everyday product use. This applied engineering role spans model behavior, orchestration, infrastructure, and user experience, with ownership from early problem definition through production operation. The product direction is to make communication and coordination more useful through proactive AI that requires little or no prompting.

Responsibilities - Develop and ship AI features across models, supporting systems, and user-facing behavior. - Design and refine prompts, tools, memory, and agent workflows. - Convert model responses into structured, predictable outputs suitable for product use. - Investigate problems across model behavior, orchestration, infrastructure, and experience layers. - Improve latency, operating cost, and reliability of production AI features. - Create lightweight evaluation methods and use real-world results to guide iteration. - Partner with product and engineering colleagues to turn loosely defined needs into working systems.

Requirements - Strong grounding in machine learning and modern neural network architectures. - Practical experience training, fine-tuning, or deploying machine learning models. - Ability to write clean, production-quality code and work across model, infrastructure, and product layers. - Sound problem-solving skills in ambiguous, fast-moving work. - A bias toward shipping, learning from usage, and making measured improvements.

Nice to have Relevant tools and technologies include Python, PyTorch or JAX, language models, model serving systems, and vector databases. The role may involve a small, hands-on team that makes decisions collectively and balances fast delivery with continued learning.