Principal Machine Learning Engineer (AI)

TL;DR

Principal Machine Learning Engineer (AI): Turning research into production-grade ML systems for a proactive AI that understands context, plans actions, and carries work forward with an accent on end-to-end pipelines for data, training, evaluation, inference, and deployment. Focus on fine-tuning models with LoRA/QLoRA/SFT/DPO, architecting scalable inference systems, GPU optimization, and integrating into backend/mobile/desktop products.

Location: Remote (Singapore)

Company

hirify.global's A1 team is building a proactive AI system that understands conversations, plans actions, and advances work over time.

What you will do

• Build and own end-to-end ML pipelines spanning data, training, evaluation, inference, and deployment.
• Fine-tune models using LoRA, QLoRA, SFT, DPO, and distillation.
• Architect scalable inference systems balancing latency, cost, and reliability.
• Design data systems for synthetic and real-world training data.
• Implement evaluation pipelines for performance, robustness, safety, and bias.
• Own production deployment with GPU optimization, memory efficiency, and scaling.
• Collaborate with application engineering to integrate ML into products.

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 PyTorch or JAX, and ability to learn others quickly.
• Experience with distributed training/inference frameworks like DeepSpeed, FSDP, Megatron, ZeRO, Ray.
• Strong software engineering for robust, production-grade systems.
• Experience with GPU optimization including memory efficiency, quantization, mixed precision.
• Comfort owning ambiguous, zero-to-one ML systems end-to-end.

Nice to have

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

Culture & Benefits

• Small, high-talent-density, hands-on team making collective decisions at rapid speed.
• Balance between shipping high-quality work and learning through iteration.
• Bring structure, exercise judgment, and execute independently.

Hiring process

• Applications evaluated by technical team; 3-4 interviews via virtual meetings and/or onsite.
• Value transparency and efficiency with prompt decisions.
• Offers extended to those demonstrating exceptional skills and mindset.