Applied AI Engineer

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

Job Duties:

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

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.

Collaborates effectively with engineers, product, and research teams to deliver reliable ML-powered features.

Iterations on models and systems are driven by real-world signals and measurable improvements.

Technical experience/Requirements:

Python, PyTorch / JAXLLMs (OpenAI-style APIs, LLaMA, Qwen, etc.), Inference / serving (e.g. vLLM), Vector DB

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

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