Background & Experience
Academic & Early Career
Growing up in Pakistan, my fascination with technology began with my father's computer, which I saw as an endless source of play and creation. After moving to Vancouver, I acquired a used laptop and taught myself programming. During high school, I developed software tools for personal use and shared them online, occasionally finding unexpected popularity. My first computer was a self-built project, assembled over a year, starting with a motherboard purchased from Fry's Electronics in Palo Alto.
Although I have strong coding skills, I chose to pursue a degree in Mechatronics Engineering at Waterloo instead of a software-focused program. My reasoning was that I could further develop my software development abilities on my own, making a university education more valuable in a field where I had less prior knowledge.
I aimed to expand my understanding in areas that genuinely interested me, particularly physics and mathematics. Initially, I considered studying physics at Caltech because I enjoyed the conceptual thinking it involved. However, I ultimately decided that electrical and mechanical engineering would provide a more practical application of those interests. Mechatronics presented the ideal combination of my existing software skills and my desire to better understand the physical world.
Waterloo's co-op program proved transformative, enabling six distinct internships that shaped my technical understanding across multiple domains. At Blackberry, I developed an internal reporting tool that streamlined engineering project management processes. At Toyota Manufacturing, I created a vision system that tracked tagged objects in 3D space on assembly lines, a patented innovation that monitored tool movements along predefined paths to verify manufacturing quality. My work at Thalmic Labs (which became North, later acquired by Google) provided early-stage startup experience as engineer number four after their Series A funding.
The program also facilitated my research internship at Stanford, followed by two positions at Apple. The first involved detailed mechanical and electromechanical design in a product engineering team, providing rigorous exposure to professional engineering processes. My final internship came after being recruited to Apple's then-nascent autonomous vehicle project when the team was just about 10 people. After interviewing, I returned to complete my academic semester before starting with them. When I returned to begin the internship several months later, the project had rapidly expanded to 50–60 people. This sequence of experiences provided practical exposure across automotive systems, consumer electronics, manufacturing processes, machine vision, and building autonomous systems.
Building a Company
I’ve always wanted to work on something that made a larger impact on the world. Several of my family members work in the medical field, so I always thought I’d start something in that industry, mostly because the tooling is really dated and the problems are evergreen and multivariate. I actually started a company right after college, Scintilla, where we built a contactless ultrasound system using photo-acoustics and vibrometry. I learned that while it’s important to develop at the edge of research and science, it’s just as important to solve a critical need—and to iterate in the market.
Julie and I started working together on an unrelated project during the early days of quarantine in the pandemic. We had a revenue-generating business in six weeks, and I realized that sales and marketing are just as first-principled as engineering. Julie had spent her career selling to large organizations and understanding organizational psychology, so naturally we were a great fit as a team. We ideated around what we'd want to tackle over a longer timeframe and found that building tools for hardware engineers created a perfect overlap of our experiences. My background working directly on these engineering teams, combined with Julie's expertise in enterprise sales, created a complementary foundation for addressing the challenges these technical professionals face.
Dystr
We are building an AI-native computation environment for engineers working on complex physical systems to write, reproduce, and share analysis. Previously, extracting computational value required significant software experience. Now that AI has lowered this knowledge barrier, the opportunity lies in writing, versioning, and managing calculations while keeping them associated with specific projects.
Mechanical, electrical, and manufacturing engineers need to maintain calculations, datasets, and contextual documents within cohesive project frameworks. These teams develop computational and data processing workflows rather than full software applications. Their goal isn't building complex interfaces but creating and using deterministic computations with minimal programming knowledge—such as physics-based calculations or analyzing oscilloscope datasets within discrete projects.
For many customers, learning sufficient code for computational analysis has presented a persistent barrier. They typically resort to MATLAB only in extreme cases, otherwise defaulting to paper, pen, and TI-83 calculators. With Dystr, they perform engineering calculations while maintaining documentation, sharing with teams, and facilitating reviews. This capability extends naturally to automating manufacturing data analysis or any recurring engineering process. Our customers create computational tasks that process periodic datasets, such as measurements from vendors monitoring assembly lines.
The hardware engineering context presents unique requirements compared to software development. Consider field failure diagnostics: Dystr allows engineers to rapidly populate a model with relevant project materials for time-sensitive troubleshooting. This efficiency provides substantial value to customers working under tight deadlines or managing multiple simultaneous projects.
In essence, we're developing an “agentic notebook” or “engineering CRM” where professionals maintain calculations, notes, datasets, and external references while collaborating with AI models on their engineering analysis. This integrated environment transforms how engineers interact with computational tools and project knowledge.
Landscape of Engineering Analysis Tools
The analysis tools available to engineers working on physical solutions haven't evolved significantly in decades. While software development has seen continuous innovation in workflows and tools, engineers designing circuit boards, mechanical systems, or manufacturing processes still rely on outdated approaches. It's surprisingly common to find engineers responsible for critical systems—from aircraft components to automotive safety features—performing calculations on TI-83 calculators and paper notebooks.
At Boeing, for example, engineers routinely spend hours manually processing CSV files from experiments, comparing results through intensive manual effort rather than through integrated computational workflows.
MATLAB was the last significant platform focused on non-programming engineers, but it still requires substantial coding knowledge. Specialized tools like ANSYS or COMSOL address narrow applications but don't solve the broader computational needs.
A critical bottleneck is that existing platforms either demand programming expertise that most engineers lack or offer limited analytical capabilities. This forces organizations into inefficient workarounds: having software engineers build custom tools, maintaining outdated systems, or simply accepting the productivity limitations. The result is significant wasted engineering talent, slower innovation cycles, and knowledge that remains trapped in isolated notebooks or individual workstations.
Current Use Cases & Customer Outcomes
We’ve been surprised by the diversity of engineering fields that are adopting Dystr beyond our initial focus on mechanical and electrical engineering.
Civil engineers working on structural analysis have found significant value in our platform despite using different data types and calculations. One civil engineering firm that conducts forensic investigations has reshaped its reporting process using Dystr. Previously, writing their detailed 100–200-page reports required 40+ hours of engineering time. They've now implemented a workflow that automatically retrieves relevant analyses from their document library based on investigation findings, cutting report creation time by orders of magnitude.
Another unexpected application comes from manufacturers analyzing test data. They've created workflows where measurements from manufacturing partners arrive via email and are automatically processed through Dystr, generating analysis reports without manual intervention.
Major Challenges
Our primary product challenge involves creating interfaces that balance computational power with accessibility for engineers without programming backgrounds. This requires precise design decisions about how to present complex functionality without overwhelming users or sacrificing analytical capabilities.
The investment landscape presents an ongoing structural challenge for companies building software for hardware engineers. Despite recent improvements, capital allocation in this sector remains asymmetric compared to pure software ventures. We've been fortunate to partner with investors like Long Journey Ventures, Omni, and angels who understand the sector's nuances, but this friction persists in the broader funding ecosystem.
Simultaneously, we're navigating the rapid advancement of AI technologies. Each quarter introduces new capabilities that could enhance our product's core functions, requiring systematic evaluation of which advances meaningfully transform engineering workflows versus those offering marginal improvements. This technical evolution demands disciplined development planning to maintain coherent architecture while integrating transformative capabilities.
Long-Term Roadmap
We envision Dystr becoming the central computational backbone for engineering teams working on physical systems. Our platform will evolve into an "engineering team in a box" with a deeply thought-through series of applications designed specifically for technical professionals.
The Dystr environment will progressively handle more routine analytical tasks, freeing engineers to tackle more challenging problems and expand their technical scope. This will create a new generation of engineers who can work across traditional domain boundaries because computational limitations and knowledge silos no longer constrain them.
An electrical engineer might transition smoothly from circuit board design to creating test fixtures for the board to overseeing the manufacturing process, with Dystr providing continuous computational support throughout. This interconnected workflow represents a shift from isolated specializations to more integrated engineering practices.
Automating repetitive work that currently consumes valuable time allows engineers to focus on higher-complexity work and creative problem-solving. The result will be broader technical capabilities for individuals and more cohesive engineering solutions for complex physical systems.
Q&A
Favorite Invention
Instead of focusing on a single achievement, I'm intrigued by the cumulative engineering advances that we often take for granted. For example, modern water and electrical infrastructure have transformed what were once extraordinary capabilities into everyday necessities.
The development of reliable infrastructure is not the result of a single breakthrough; rather, it is the culmination of thousands of incremental innovations in materials science, manufacturing processes, and design methodologies.
What is truly remarkable is not just the technical achievements themselves, but how they have enabled capabilities that would have seemed extraordinary just a generation ago to become commonplace tools. Everything surrounding us—from the devices on our desks to the systems powering our cities—represents impressive engineering accomplishments that we now consider ordinary. This pattern of transforming extraordinary abilities into everyday utilities exemplifies the most impactful nature of engineering.
Favorite Interview Question
I don't have a standard question, but I focus on understanding a candidate's first-principles thinking. When working with emerging technologies like AI, experience with specific implementations matters less than fundamental understanding. I often introduce a basic concept relevant to the role and explore how deeply the candidate can analyze it.
This reveals whether they've memorized surface knowledge or genuinely understand underlying principles. For engineering roles, this might involve discussing material properties or circuit behaviors; for product positions, it might be user interaction patterns. What I'm evaluating isn't specific knowledge but their ability to reason from foundations to applications—a critical skill when working with rapidly evolving technologies.
If you’re curious to learn more about Dystr, you can reach Nabeel on LinkedIn or try their platform at dystr.com
