Senior Machine Learning Engineer

SingaporeRemoteFull-Timesenior

Machine Learning EngineerData

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Quick Summary

Overview

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,

Technical Tools

Machine Learning EngineerData

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

~4 min read

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.

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.

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, GCP, or Azure) and containerization (Docker, Kubernetes).

Familiarity with real-time data processing, anomaly detection, and time-series forecasting in production.

Experience with large datasets and big data technologies like Spark and Kafka to build scalable solutions.

First-principles thinking and strong problem-solving, with a proactive approach to challenges.

A self-starter who takes ownership end to end and works autonomously to drive results.

Excellent communication, with the ability to convey complex technical concepts clearly and a strong customer-obsessed mindset.

Globally distributed, remote-first flexibility: Work with a lean, distributed team across Asia and Europe, built on trust, accountability, and collaboration. Our diversity of perspectives fuels innovation and keeps us curious.

Tech-first team: You'll work with like-minded people who care about solving hard problems with technology. Engineering and data are at the core of what we do.

Real ownership from day one: We're a company of ~30, and the data science team is just two: you and one other. No layers, no waiting to be unblocked. You'll own ML systems end to end, from research to production, and grow fast because there's nowhere to hide and everywhere to make a mark.

Impact you can see: In a company this size, your work moves the business directly. You'll watch your ideas and decisions ship and matter, not disappear into a backlog.

Curiosity: We stay close to the data and to model behaviour before we trust an output. We dig into why a model does what it does, not just whether the metric moved.

Ownership: We act like founders. We take a model from research to production and stay on it, monitoring, debugging, and improving long after it ships.

Raising the bar: We don't settle for a model that works in a notebook. We aim for systems that are reliable, scalable, and cost-aware in production.

Effective: We focus on models that create real product and customer impact, not accuracy for its own sake.