Principal Machine Learning Engineer

AI Chopping Block; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

AI Chopping Block is seeking a senior ML systems engineer to own the training pipelines, inference systems, and deployment for AI-powered workflows. You will work on end-to-end ML architectures, from data systems to production-grade models, with an emphasis on reliability and scalability.

You will fine-tune and deploy large models using modern methods (LoRA/QLoRA/SFT/DPO), optimize GPU usage, and collaborate with backend teams to ship robust AI features across products.

Qualifications

• 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 at least one modern ML framework (e.g. PyTorch, JAX).
• Experience with distributed training and inference frameworks (e.g. DeepSpeed, Ray).
• Strong software engineering fundamentals for production-grade systems.
• Experience with GPU optimization including memory efficiency and mixed precision.
• Comfort owning end-to-end ML systems in zero-to-one contexts.
• Bias toward shipping and iterative improvements.

Responsibilities

• Build and own end-to-end ML pipelines spanning data, training, evaluation, inference, and deployment.
• Fine-tune and adapt models using state-of-the-art methods (LoRA, QLoRA, SFT, DPO, distillation).
• Architect and operate scalable inference systems balancing latency, cost, and reliability.
• Design and maintain data systems for synthetic and real-world training data.
• Implement evaluation pipelines covering performance, robustness, safety, and bias.
• Own production deployment including GPU optimization, memory efficiency, latency reduction, and scaling policies.
• Collaborate with application engineering to integrate ML systems into backend/mobile/desktop products.
• Make pragmatic trade-offs and ship improvements quickly.

Skills

Deep Learning

Transformer models

Production ML

PyTorch

JAX

Distributed training

GPU optimization

Tools

DeepSpeed

Ray

TensorRT

Job description

About A1

There are over 5 billion users using basic applications today such email, notes, tasks that are not AI-native. Our mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows, with minimal prompting.

Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Our objective is to help users complete tasks daily enjoyable with over ~90%* reduced time.

Role

You will be responsible for turning research direction into working, production-grade ML systems. This role owns the execution layer of A1's intelligence - training pipelines, inference systems, evaluation tooling, and deployment.

Focus

• Build and own end-to-end ML pipelines spanning data, training, evaluation, inference, and deployment.

• Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation.

• Architect and operate scalable inference systems, balancing latency, cost, and reliability.

• Design and maintain 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.

• Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.

• Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products.

• Make pragmatic trade-offs and ship improvements quickly, learning from real usage.

• Work under real production constraints: latency, cost, reliability, and safety

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 at least one modern ML framework (e.g. PyTorch, JAX), and ability to learn others quickly.

• Experience with distributed training and inference frameworks (e.g. DeepSpeed, FSDP, Megatron, ZeRO, Ray).

• Strong software engineering fundamentals - you write robust, maintainable, production-grade systems.

• Experience with GPU optimization, including memory efficiency, quantization, and mixed precision.

• Comfort owning ambiguous, zero-to-one ML systems end-to-end.

• A bias toward shipping, learning fast, and improving systems through iteration.

Ideal Experience

• Experience with LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer.

• Contributions to open-source ML or systems libraries.

• Background in scientific computing, compilers, or GPU kernels.

• Experience with RLHF pipelines (PPO, DPO, ORPO).

• Experience training or deploying multimodal or diffusion models.

• Experience with large-scale data processing (Apache Arrow, Spark, Ray).

How We Work

The best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product

Interview process

If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.

Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.

We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.