Senior Machine Learning Engineer

Own ML and AI solutions end to end: frame the problem, build the model, deploy it, and run it in production (MLOps). You own the full lifecycle, not just the modelling.

Own some products end to end and contribute to others, thinking about business outcomes, cost, and scale, and model performance.

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.

Collaborate with Data, Engineering, and Product to understand the business goals behind what you are building, so what you ship actually moves the metrics that matter.

Build, test, deploy, and monitor real-time prediction models and ML algorithms that address the key business problems our product focuses on: visibility, prediction, demand forecasting, freight audit, using MLOps best practices with version control and performance tracking.

Productionise LLM-based systems where relevant, treating prompts and model behaviour as engineering artifacts.
Perform feature engineering, model tuning, and validation so models are production-ready and optimized for performance.

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

5+ years building, deploying, and scaling machine learning models in production, with a track record of owning things end to end.

A business and software mindset over pure research. You think about business outcomes, cost, and impact, and you know when "good enough and shipped" beats a perfect model. Backgrounds that fit well include software engineers who moved into AI/ML, or ML engineers used to owning products end to end in a lean team.

Prior experience in a lean product-based startup environment: a small ML/DS team where everyone wears many hats, with no big R&D lab and no clean specs or baseline data handed to you. You create clarity and make progress without every answer upfront. 

Experience in logistics tech or supply chain tech, ideally building AI-powered intelligence for freight forwarding, is a strong plus. 

Experience 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.

Strong Python and SQL, with experience in cloud platforms (AWS) and containerization (Docker, Kubernetes). 

Proven experience owning the full lifecycle, from R&D to production and MLOps, in fast-paced environments. You can build a model and run it in production yourself. 

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, evaluation harnesses, guardrails, and monitoring output quality, latency and cost in live systems.

First-principles thinking, a self-starter mentality, and the ability to take ownership end to end and work autonomously.

Excellent communication, with the ability to convey complex technical concepts clearly, and a strong customer-obsessed mindset. You are genuinely curious about our industry, our customers, and the logistics domain, and you let that shape what you build.

Own a high-leverage problem: your work drives real product and business outcomes, not research for its own sake.

Real ownership from day one: we're 30 people and the data science team is small, so there are no layers, nowhere to hide and everywhere to make a mark. You own ML systems end to end and grow fast.

Tech-first team: you'll work with people who care about solving hard problems with technology, and get exposure to complex product problems across software, data, ML, and logistics.

Globally distributed, remote-first flexibility: work with a fully distributed team across Asia and Europe, built on trust, accountability, and collaboration.