Responsibilities
- 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
- 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
- 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 cases
- Ensure engineering effort maps directly to business impact
- 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
- 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
Requirements
- 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
- 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
- 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
Nice to Have
- 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
Benefits
- 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.
Work Arrangement
Remote-first, globally distributed across Asia and Europe
WHAT SUCCESS LOOKS LIKE IN THIS ROLE
- 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.
WHAT WE’RE LOOKING FOR
- 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
Our CORE Values Guide Everything We Do
- 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.
