Machine Learning Platform Engineer

AI Chopping Block; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

A1 is building proactive AI capabilities and seeks an ML Platform Engineer to power the AI stack. You will design and operate systems behind the AI, from training to deployment, inference, observability, and continuous improvement.

You will collaborate with AI researchers and product engineers to ship reliable, scalable production systems, develop pipelines for data prep, training, evaluation, and model release, and push for faster experimentation and cost efficiency.

Qualifications

• Strong software engineering fundamentals and experience building production systems.
• Experience building ML infrastructure, platforms, or production machine learning systems.
• Experience with model deployment, inference, evaluation, or data pipelines.
• Strong understanding of distributed systems and system reliability.
• Ability to write clean, maintainable, production-quality code.
• Comfortable working in ambiguous, fast-moving environments.
• Bias toward ownership, experimentation, and continuous improvement.

Responsibilities

• Build and operate the ML infrastructure and platforms powering A1’s AI products.
• Design systems for model training, evaluation, deployment, inference, and experimentation.
• Build and optimise model serving and inference infrastructure for high-throughput and low-latency workloads.
• Improve reliability, scalability, latency, and cost efficiency of AI systems.
• Develop reliable pipelines for data preparation, training, evaluation, model release, and continuous improvement.
• Build platforms and tooling that enable AI engineers and researchers to experiment, evaluate, and ship models faster.
• Develop evaluation and benchmarking infrastructure to measure model quality, performance, and regressions.
• Build production observability, monitoring, tracing, and alerting for AI/ML workloads.
• Improve AI systems across reliability, scalability, latency, throughput, and cost.

Skills

Strong software engineering

Production systems

Distributed systems

Ambiguity tolerance

Ownership mindset

Tools

PyTorch/JAX

vLLM/SGLang/TensorRT-LLM

Cloud infrastructure

GPU performance tooling

Vector databases

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.

About the Role

As an ML Platform Engineer, you will build the infrastructure and systems that power A1's AI capabilities.

You will design and operate the systems behind the AI stack, from model training and evaluation to deployment, inference, observability, and continuous improvement.

You will work closely with AI engineers, researchers, and product engineers to turn models into reliable, scalable, and cost-efficient production systems. You will build the platforms, tooling, and infrastructure that enable the team to experiment quickly and bring AI capabilities to production with confidence.

Focus

• Build and operate the ML infrastructure and platforms powering A1’s AI products




• Design systems for model training, evaluation, deployment, inference, and experimentation




• Build and optimise model serving and inference infrastructure for high-throughput and low-latency workloads




• Improve reliability, scalability, latency, and cost efficiency of AI systems




• Develop reliable pipelines for data preparation, training, evaluation, model release, and continuous improvement




• Build platforms and tooling that enable AI engineers and researchers to experiment, evaluate, and ship models faster




• Develop evaluation and benchmarking infrastructure to measure model quality, performance, and regressions




• Build production observability, monitoring, tracing, and alerting for AI/ML workloads




• Improve AI systems across reliability, scalability, latency, throughput, and cost




• Identify bottlenecks across the ML stack and continuously improve system performance




• Work closely with AI engineers, researchers, and product teams to turn evolving model requirements into production-ready infrastructure

Tech Stack

• Python




• PyTorch / JAX




• LLM and ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLM




• Cloud infrastructure




• Distributed systems




• ML/data pipelines and workflow orchestration




• GPU infrastructure and performance tooling




• Vector databases and retrieval infrastructure

Ideal Experience

• Strong software engineering fundamentals and experience building production systems




• Experience building ML infrastructure, platforms, or production machine learning systems




• Experience with model deployment, inference, evaluation, or data pipelines




• Strong understanding of distributed systems and system reliability




• Ability to write clean, maintainable, production-quality code




• Comfortable working in ambiguous, fast-moving environments




• Bias toward ownership, experimentation, and continuous improvement

Outcomes

• AI infrastructure reliably supports production workloads at scale




• Models can be trained, evaluated, deployed, and improved efficiently




• Inference systems deliver strong latency, throughput, reliability, and cost efficiency




• ML pipelines are reproducible, observable, maintainable, and robust




• Model and infrastructure regressions are detected quickly and diagnosed efficiently




• Common ML infrastructure capabilities become reusable platform primitives rather than being rebuilt for every AI product




• The AI stack can evolve rapidly as new models, architectures, and inference techniques emerge