applied ai engineer - 外企居家 英文流利.

about the company.
MNC AI-related Company

about the team.
R & D and AI

about the job.

• 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

skills and experience required.

• 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

tech stack.

• Python

• PyTorch / JAX

• LLMs (OpenAI-style APIs, LLaMA, Qwen, etc.)

• Inference / serving (e.g. vLLM)

• Vector DB

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.

• 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.