Senior Machine Learning Engineer

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, 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.

What's In It For You:

• 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.

Our CORE Values Guide Everything We Do:

• 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.