Applied AI Engineer

Das ist der Job

As an Applied AI Engineer, you will turn model capabilities into real product behavior.

Darum lohnt es sich

Focus 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 Python PyTorch / JAX Inference / serving (e.g. vLLM) Vector DB Ideal Experience Strong foundation in machine learning and modern neural network architectures.

Collaborates effectively with engineers, product, and research teams to deliver reliable ML‑powered features. How We Work The best products today in the world were built by small, world class teams. Joining our team requires the ability to bring structure, exercise judgment, and execute independently.

A1 is building a proactive AI system that understands context across conversations, plans actions, and carries work forward over time. You will own problems end-to-end, from shaping model behavior, to building the systems around it, to ensuring it performs reliably in production.

This role sits at the intersection of machine learning, systems, and product, focusing on making AI actually work for users, not just in demos, but in real-world usage.

Hands‑on experience with training, fine‑tuning, or deploying ML models Ability to write clean, production‑quality code Strong problem‑solving skills in ambiguous, fast‑moving environments Bias toward shipping, iteration, and continuous improvement Outcomes ML models in production meet expected accuracy, latency, and reliability targets.

Production issues are identified quickly, debugged effectively, and root causes addressed. Data pipelines, training loops, and inference systems are robust, reproducible, and maintainable. Iterations on models and systems are driven by real‑world signals and measurable improvements.

We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Our goal is to put in hands of our users a truly magical AI product. #J-18808-Ljbffr