Software Engineering, ML, and Analytics operate as a clear, structured, and high-performing unit with defined ownership and strong delivery predictability.
Technical decisions are grounded in first-principles thinking, scalable architecture, and long-term product clarity.
AI and data capabilities are consistently deployed into production and directly tied to measurable business impact.
Engineering Leadership & Architecture
Own technical direction across platform, infrastructure, and product systems
Define scalable architecture and ensure reliability, performance, and security
Improve engineering velocity, code quality, and delivery discipline
Strengthen documentation, ownership, and system clarity
Mentor tech team: Engineering, ML, Analytics
AI, Data Science & Analytics Strategy
Define how AI and data drive competitive advantage in our product
Ensure ML models move from experimentation into production environments
Strengthen MLOps, data pipelines, and real-time data systems
Align analytics insights with product and customer decision-making
Ensure AI investments translate into real outcomes, not just experiments
Product & Business Alignment
Partner closely with Product to shape technical roadmap
Work with Sales and Customer teams to understand enterprise requirements
Guide solution design for complex customer use casesEnsure engineering effort maps directly to business impact
Team, Culture & Organizational Development
Lead and develop Engineering, ML/Data Science, and Analytics teams
Hire strong senior talent and raise the technical bar
Create clarity in roles, responsibilities, and technical ownership
Reduce single-point dependency across critical systems
Build a culture of accountability, curiosity, and effectiveness
Hands-On Technical Leadership
Stay close to architecture and key system decisions
Review critical designs and technical proposals
Step into complex technical problems when needed
Prototype or validate high-impact ideas
Lead by example in technical depth and problem solving
10+ years in software engineering, data, or ML environments
Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related field
Software heavy background who understands data, AI, system design and infrastructure
Experience leading technical teams in a startup or product-driven B2B SaaS company
Track record building and scaling production AI/ML systems
Experience with distributed systems, real-time data, and cloud infrastructure
Background in logistics or supply chain industry, or data-heavy SaaS is a strong plus
Previous experience working in enterprises/fortune 500 is a plus
Technical Depth
Curious by Default: You dig deeper instead of following instructions to the letter. When something in the system surprises you, you chase the why, not just the fix. You treat every layer of the stack as something worth understanding, and you ask the good questions early
Strong backend and system architecture expertise
Experience deploying ML/AI systems into production, not just research
Deep understanding of MLOps, data platforms, and model lifecycle management
Comfortable operating across engineering, data science, and analytics domains
Leadership & Culture
First-principles thinker who breaks down complex problems clearly
High ownership and accountability
Builder mindset with strong technical judgment
Clear communicator across technical and business teams
Focused on effectiveness and real outcomes
Globally distributed, remote-first flexibility: Work with a fully 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 individuals who share a passion for solving difficult problems using technology.
Accelerated growth: Compress the learning curve in a couple of years by owning the web app from day one as your own baby. We are building our company to be the next B2B market leader in predictive global supply chains and you’ll be a major part of our story.
Impact you can see: With a lean structure, your work is effective from the start. You’ll see the results of your ideas and decisions directly moving the business forward.
Curiosity: We read the code before we trust it. We dig into why a system behaves the way it does, not just how to make the error go away.
Ownership: We act like founders. We don't wait for a ticket to fix what's broken, and we stay on a problem until it's actually solved in production.
Raising the bar: We do not settle for code that only works locally or solves the immediate request. We aim for backend systems that are reliable, scalable, maintainable, and easy for the team to reason about.
Effectiveness: We focus on engineering work that creates real product and customer impact. We prioritize the right problems, make practical trade-offs, and ship solutions that improve outcomes, not just output.
