The role

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 an ML Platform Engineer, you will build the infrastructure and systems that power ActAI'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.

What you'll do

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

About Bjak

Bjak is a leading online insurance platform in Southeast Asia, operating primarily through its flagship brand, Bjak, which allows users to compare and purchase insurance policies digitally. The company has expanded into financial services, including investment and lending products, and is now developing an AI-powered neobank. With a strong focus on technology and innovation, Bjak has grown rapidly since its founding in 2019, serving millions of users across Malaysia and other regional markets.

Bjak has established a significant presence in Spain, with a large number of open roles based there, particularly in engineering, product, and design. The company is building a diverse, remote-friendly team and is actively hiring international talent for its AI finance agent and neobank initiatives. For international professionals, Bjak offers the opportunity to work on cutting-edge AI applications in fintech, with a multicultural environment and the chance to contribute to a fast-growing startup.

Founded: 2019
Employees: 500–1,000
Headquarters: Kuala Lumpur, Malaysia
In Spain: Madrid

Good to know if you are moving

Bjak is headquartered in Kuala Lumpur, Malaysia, but has a substantial remote-friendly culture and is actively hiring in Spain, indicating a global, distributed team.
The company is building an AI neobank and AI finance agents, offering engineers and product professionals the chance to work on cutting-edge AI applications in fintech.
Bjak has raised over $100 million in funding, indicating strong financial backing and growth potential.
The company's workforce is diverse, with employees from various countries, making it a welcoming environment for international hires.
Bjak offers competitive compensation and benefits, including equity, to attract top talent globally.

All open roles at Bjak

Machine Learning Platform Engineer

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