An Applied AI Engineer role turning model capabilities into real product behavior — from shaping model behavior, through building the surrounding systems, to ensuring production reliability.
Responsibilities
- ▹Build and ship AI features end-to-end (model → system → user experience)
- ▹Design and iterate on prompts, tools, memory, and agent workflows
- ▹Turn raw model outputs into structured, reliable, and predictable behaviors
- ▹Debug issues across the full stack (model, orchestration, infra, UX)
- ▹Optimize for latency, cost, and production reliability
- ▹Develop lightweight evaluation frameworks to measure real-world performance
- ▹Work closely with product and engineering to translate ambiguous problems into working systems
Requirements
- ▹Strong foundation in machine learning and modern neural network architectures
- ▹Hands-on experience with training, fine-tuning, or deploying ML models
- ▹Ability to write clean, production-quality code
- ▹Comfort working across abstraction layers (model → infra → product)
- ▹Strong problem-solving skills in ambiguous, fast-moving environments
- ▹Bias toward shipping, iteration, and continuous improvement
Soft skills
Structured thinking and independent judgmentClose collaboration with engineering, product, and research teamsCommitment to measurable, real-world improvement
About the company
The company (A1) is building a proactive smart assistant for everyday users, bringing intelligence to conversations, errands, organizing, and workflows with minimal prompting. The product focuses on reliability for long-running workflows, persistent context, and real-world task completion, aiming to cut users' daily task time by roughly 90%. The team is small, high-talent-density, and hands-on, making decisions collectively and moving at rapid speed.
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