Machine Learning Engineer (AI)

Location: Remote from China; address in Beijing, China. Interviews may be conducted virtually or onsite.

Company

hirify.global is building a proactive AI smart assistant for conversations, errands, organization, and everyday workflows.

What you will do

• Build and own end-to-end ML pipelines across data, training, evaluation, inference, and deployment.
• Fine-tune and adapt transformer-based models using LoRA, QLoRA, SFT, DPO, and distillation.
• Architect scalable inference systems while balancing latency, cost, reliability, and safety.
• Design data systems for synthetic and real-world training data and implement evaluation for performance, robustness, safety, and bias.
• Own production deployment, including GPU optimization, memory efficiency, quantization, latency reduction, and scaling.
• Collaborate with application engineering to integrate ML systems into backend, mobile, and desktop products.

Requirements

• Strong background in deep learning and transformer-based architectures.
• Experience training, fine-tuning, or deploying large-scale ML models in production.
• Proficiency with a modern ML framework such as PyTorch or JAX.
• Experience with distributed training and inference frameworks such as DeepSpeed, FSDP, Megatron, ZeRO, or Ray.
• Strong software engineering fundamentals and experience building robust, maintainable, production-grade systems.
• Experience with GPU optimization, including memory efficiency, quantization, and mixed precision, plus the ability to own ambiguous ML systems end-to-end.

Nice to have

• Experience with vLLM, TensorRT-LLM, or FasterTransformer.
• Open-source contributions to ML or systems libraries.
• Background in scientific computing, compilers, or GPU kernels.
• Experience with RLHF pipelines, multimodal or diffusion models, or large-scale data processing with Apache Arrow, Spark, or Ray.

Culture & Benefits

• Work with a small, high-talent-density, hands-on team.
• Make decisions collectively and ship improvements quickly through iterative learning.
• Operate with autonomy, structure, and pragmatic judgment under real production constraints.
• Prompt hiring decisions after the interview process.

Hiring process

• Complete 3, and no more than 4, interviews if selected for further consideration.
• Applications are evaluated by technical team members.
• Interviews take place via virtual meetings and/or onsite.