Principal Machine Learning Engineer (AI)

TL;DR

Principal Machine Learning Engineer (AI): Building proactive AI systems and production-grade ML pipelines with an accent on model fine-tuning and inference system architecture. Focus on designing end-to-end training pipelines, scaling deployments under production constraints, and optimizing models for latency and reliability.

Location: Remote (based in China/Beijing)

Company

A technology company building proactive AI systems that understand context and facilitate long-term task execution.

What you will do

• Build and own end-to-end ML pipelines including data, training, evaluation, inference, and deployment.
• Fine-tune and adapt models using methods such as LoRA, QLoRA, SFT, DPO, and distillation.
• Architect and operate scalable inference systems while balancing latency, cost, and reliability.
• Design and maintain data systems for high-quality training data.
• Partner with research leadership to implement evaluation pipelines for performance, safety, and bias.
• Own production deployments with a focus on GPU memory efficiency and scaling policies.

Requirements

• Strong background in deep learning and transformer-based architectures.
• Hands-on experience training, fine-tuning, or deploying large-scale ML models in production.
• Proficiency with modern ML frameworks like PyTorch or JAX.
• Experience with distributed training and inference frameworks such as DeepSpeed, FSDP, or Ray.
• Experience with GPU optimization including quantization and mixed precision.
• Strong software engineering fundamentals to build maintainable, production-grade systems.

Nice to have

• Experience with LLM inference frameworks like vLLM or TensorRT-LLM.
• Contributions to open-source ML or systems libraries.
• Background in scientific computing, compilers, or GPU kernels.
• Experience with RLHF pipelines (PPO, DPO, ORPO).
• Experience with large-scale data processing tools like Apache Arrow or Spark.

Culture & Benefits

• High talent density team focused on rapid execution and collective decision-making.
• Opportunity to work on complex AI challenges with global reach.
• Collaborative environment striking a balance between research and high-quality production shipping.
• Emphasis on individual judgment, independence, and technical ownership.

Hiring process

• Evaluation by the technical team.
• 3 to 4 interviews conducted via virtual meetings or onsite.
• Prompt decision-making process.