Job Overview
Location
United States
Job Type
Full-time
Category
Engineering
Date Posted
August 11, 2026
Full Job Description
đź“‹ Description
- • 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 on building and operating 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.
🎯 Requirements
- • 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.
🏖️ Benefits
- • 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.
Skills & Technologies
See exactly how your profile matches this role — strengths, skill gaps, and what to do about them.
About Bjak Sdn. Bhd.
Bjak operates Malaysia’s largest digital auto-insurance marketplace, enabling instant price comparison and online purchase of motor coverage from leading insurers. The platform uses proprietary technology to simplify complex tariffs, deliver personalised quotes and e-policy issuance within minutes, eliminating paperwork and agent visits. Licensed by Bank Negara Malaysia, Bjak also offers road-tax renewal, accident assistance and claims support, serving millions of drivers nationwide while partnering with insurers to increase digital distribution efficiency and customer reach.
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