Senior Machine Learning Engineer

Portcast is a venture-backed, Singapore-based logistics technology startup building a real-time transportation visibility platform for global supply chains. We help shippers, manufacturers, and logistics service providers turn data into decisions and decisions into measurable business impact.

Our platform goes beyond visibility. Portcast surfaces the right risks early, helping teams prevent detention and demurrage, accelerate exception management, and close invoices faster with built-in evidence. We turn visibility into outcomes: reduced costs, improved operational control, and more predictable supply chains.

Founded in 2018 and backed by leading technology investors, we are building for an industry at a critical inflection point of digital transformation. Our team of software engineers, data scientists, and logistics experts is on a mission to make supply chains not just visible, but decisively actionable, end to end.

ABOUT THE ROLE: We are looking for a Senior Machine Learning Engineer who specializes in taking ML and AI models into production. You will own the full lifecycle, from research and model building to deployment and scaling in real-world environments. This is a hands-on role designing robust algorithms that address our core business problems, particularly in visibility, prediction, demand forecasting, and freight audit. Your focus is ensuring model accuracy, reliability, and scalability in live production systems. You'll be one of two on our data science team, so this role is built for someone highly independent, ambitious, and curious, comfortable owning problems end to end without a big team around them.

What You’ll Own:

• Develop and deploy machine learning models from initial research to production, ensuring scalability and performance in live environments.

• Own the end-to-end ML pipeline: data processing, model development, testing, deployment, and continuous optimization.

• Comfortable building from a rough outline rather than a finished spec. You'll work directly with product and customer-facing teams to turn loosely defined problems into shipped features, and re-scope quickly when priorities shift. You'll own the how, which means pushing back on a weak brief and making the call when the spec runs out. We have a strong sense of direction; the details pivot often.

• Design and implement machine learning algorithms that address the key business problems our product focuses on: visibility, prediction, demand forecasting, and freight audit.

• Ensure reliable, scalable ML infrastructure, automating deployment and monitoring using MLOps best practices.

• Perform feature engineering, model tuning, and validation so models are production-ready and optimized for performance.

• Build, test, and deploy real-time prediction models, maintaining version control and performance tracking.

WHAT WE'RE LOOKING FOR:

• Bachelor's, Master's, or PhD in Computer Science, Engineering, or a related field.

• At least 5+ years of end-to-end and consistent building, deploying, and scaling machine learning models in production environments.

• Hands-on experience productionising LLM-based systems. Bonus points for designing AI agents and multi-step workflows, tool/function calling, and grounding models on proprietary data through retrieval and context design - and treating prompts and model behaviour as engineering artifacts: versioning and prompt management, evaluation harnesses, guardrails, and monitoring output quality, latency and cost in live systems. We care about how you reason about system behaviour, reliability, and cost.

• Proven experience across the full product lifecycle, taking models from R&D to deployment in fast-paced environments.

• Experience in a product-based company, preferably a startup with early-stage technical product development.

• Strong expertise in Python and SQL, with experience in cloud platforms (AWS,...