Staff Machine Learning Engineer
RemoteSingapore
Job Summary
Own end-to-end ML system execution including data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation. Architect scalable inference systems balancing latency, cost, and reliability while designing data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias in partnership with research leadership. Collaborate with application engineering to integrate ML systems into backend, mobile, and desktop products, making pragmatic trade-offs to ship improvements quickly under real production constraints.
Required Qualifications
- Python
- PyTorch / JAX
- GPU-based training and inference system
- You have built or shipped real ML systems used by people, not just demos
- You are comfortable working with large models and understanding their failure modes
- You write strong, production-grade code and care about system correctness
- You are self-directed, pragmatic, and take full ownership of outcomes
- You communicate clearly and collaborate well in small, high-trust teams
- The ability to bring structure, exercise judgment, and execute independently
Desired Qualifications
- Experience with state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation
- Experience architecting and operating scalable inference systems
- Experience designing and maintaining data systems for high-quality synthetic and real-world training data
- Experience implementing evaluation pipelines covering performance, robustness, safety, and bias
- Experience with GPU optimization, memory efficiency, latency reduction, and scaling policies
- Experience collaborating closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products
- Experience making pragmatic trade-offs and shipping improvements quickly, learning from real usage
- Experience working under real production constraints: latency, cost, reliability, and safety
- Experience ensuring research and models reliably translate into production-ready solutions with clear performance and quality targets
- Experience ensuring ML pipelines, training loops, and inference systems are stable, efficient, and maintainable
- Experience detecting, debugging, and resolving production issues quickly, minimizing user impact
- Experience supporting team members, aligning them, and enabling them to deliver high-impact ML work with minimal friction
- Experience ensuring iterations on models and systems are measurable, safe, and improve user experience over time
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