Lead Engineer, Machine Learning

ActAI; Singapore; On-site; SGD 180,000 - 280,000; Full time

Job summary

ActAI is seeking a Lead Engineer, Machine Learning to own the end-to-end ML systems powering our platform. You will work across data, training, evaluation, inference and deployment to turn research into reliable production solutions.

You will architect high-performance inference, build training pipelines for large models, and collaborate with research and application engineering to ship measurable improvements at scale.

Qualifications

• Built or shipped real ML systems used by people, not just demos.
• Experience with large models, training, evaluation and inference.
• Strong production-grade coding with emphasis on reliability.

Responsibilities

• Own end-to-end ML systems from data and training to evaluation, inference and deployment.
• Build and evolve training and fine-tuning pipelines for large models.
• Design evaluation systems for capability, safety and product performance.
• Architect high-performance inference systems with emphasis on latency and cost.
• Create data pipelines and production infrastructure for training data.
• Collaborate with research and application engineering to ship improvements.

Skills

Production ML systems

Large-model training

Python

PyTorch

System design

GPU-based training & inference

Tools

PyTorch

JAX

CUDA

Job description

There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.

Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. We believe products will greatly reduce hallucinations

Our objective is to organise anyone's life, allowing us all to spend time on valuable and meaningful things

As Lead Engineer, Machine Learning, you own the execution layer of our intelligence, turning research and model capabilities into reliable, scalable production systems.

You will work across the model lifecycle: data, training, evaluation, inference, and deployment. This is a hands-on leadership role for someone who wants to operate at the intersection of research, systems, and product.

What You'll Own

• Own the end-to-end ML systems powering our company, from data and training to evaluation, inference, and deployment.
• Build and evolve training and fine-tuning pipelines for large models.
• Design evaluation systems that measure capability, robustness, safety, and real-world product performance.
• Architect high-performance inference systems, optimizing latency, GPU utilization, memory, cost, and reliability.
• Build data pipelines and systems for high-quality real-world and synthetic training data.
• Establish reliable production infrastructure for deploying, monitoring, and continuously improving models.
• Partner closely with research and application engineering to turn model capabilities into product improvements.
• Make pragmatic technical trade-offs and rapidly iterate based on real-world performance.

What We're Looking For

• Experience building and shipping ML systems used in production, not just research prototypes.
• Strong understanding of modern large-model training, fine-tuning, evaluation, and inference.
• Strong software engineering and systems fundamentals.
• Experience operating ML workloads at meaningful scale, particularly GPU-based systems.
• Strong technical judgment and the ability to navigate ambiguous problems independently.
• A bias toward experimentation, measurement, and shipping.
• High standards for correctness, reliability, and production quality.

Outcomes

• Research and models reliably translate into production-ready solutions with clear performance and quality targets.
• ML pipelines, training loops, and inference systems are stable, efficient, and maintainable.
• Production issues are detected, debugged, and resolved quickly, minimizing user impact.
• Team members are supported, aligned, and able to deliver high-impact ML work with minimal friction.
• Iterations on models and systems are measurable, safe, and improve user experience over time.
• Python
• PyTorch / JAX
• GPU-based training and inference system

Ideal Experience

• 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.

How We Work

We are a small, high-talent-density, hands-on team. Engineers have broad ownership and are expected to exercise strong judgment and execute independently.

We make decisions quickly, work closely together, and balance speed with engineering fundamentals. We care less about process and more about building something exceptional.