Principal Machine Learning Engineer, AI Platform (Foundation Model...

PLT Engineering; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

A prominent AI-focused company in Singapore is seeking a Principal Machine Learning Engineer to lead the post-training strategies for foundation models. This role involves architecting scalable training pipelines, collaborating with data teams for high-quality datasets, and optimizing model performance for real-world applications. The ideal candidate will have deep learning expertise, strong leadership skills, and experience translating AI research into production systems. Join us to drive innovation and excellence in AI!

Qualifications

• Experience with large-scale foundation models and post-training strategies.
• Strong background in deep learning research and system engineering.
• Proven leadership skills in technical direction and project execution.

Responsibilities

• Define and drive the roadmap for post-training strategies.
• Design and implement robust, scalable training pipelines.
• Collaborate with data teams to create high-quality instruction sets.
• Develop comprehensive evaluation suites to measure model performance.
• Optimize training jobs for GPU utilization and cost efficiency.
• Partner with teams to translate user requirements into fine-tuning objectives.
• Translate AI research into production-grade systems.
• Provide technical mentorship and foster innovation.

Skills

Deep Learning

PyTorch

Data Strategy

Mentorship

Cross-Functional Collaboration

Tools

DeepSpeed

Ray

Megatron-LM

Job description

Get to Know the Team

The AI Platform team empowers Grab teams to leverage advanced AI seamlessly and effectively. We're building cutting‑edge tools and infrastructure to democratize AI capabilities, accelerate innovation, and enhance Grab's products and services at scale.

Get to Know the Role

As a Principal Machine Learning Engineer focused on Foundation Model Post‑Training, you'll report into the Head of Engineering, Machine Learning and Experimentation Platforms and work onsite in Grab One North Singapore office.

You'll be the technical anchor for aligning our large‑scale foundation models with human intent and domain requirements. You'll architect pipelines using Supervised Fine‑Tuning (SFT) and RLHF to transform raw base models into safe, high‑performance products for Grab. You'll also bridge deep learning research, systems engineering, and data strategy, requiring a leader to drive technical direction and execute large‑scale experiments.

The Critical Tasks You Will Perform

• Strategic Technical Leadership: Define and drive the roadmap for post‑training strategies, including SFT, RLHF (PPO/DPO/GPRO), and instruction tuning, to improve model alignment, safety, and reasoning capabilities.
• Pipeline Architecture: Design and implement robust, scalable, and distributed training pipelines using frameworks like PyTorch, DeepSpeed, Ray or Megatron‑LM to handle models with billions of parameters.
• Data Strategy & Curation: Oversee the data engine for post‑training; collaborate with data teams to design high‑quality instruction sets, manage human annotation workflows, and implement automated data filtering/deduplication techniques.
• Evaluation & Benchmarking: Develop comprehensive evaluation suites (both automated benchmarks and human‑in‑the‑loop protocols) to rigorously measure model performance, hallucination rates, and alignment drift.
• Optimization & Efficiency: Optimize training jobs for GPU utilization and cost‑efficiency, including quantization, distillation, LoRA/Q‑LoRA implementation, and memory optimization techniques.
• Cross‑Functional Collaboration: Partner with multi‑functional teams to translate user requirements into specific reward functions and fine‑tuning objectives.
• Bridge Research and Engineering: Translate the latest AI research into robust, scalable, production‑grade systems that drive tangible business outcomes.
• Mentorship: Provide technical mentorship, foster innovation, and inspire excellence across engineering, research, and product teams.