Imagine you're an aerospace engineer working to optimize a critical engine component. With traditional simulation methods, every design iteration takes weeks of costly compute time, waiting for results, and watching product launch timelines slip further out of reach. Now, imagine getting those same results in seconds, thanks to AI models that learn from and replicate the physics behind industry-standard simulation software.
That’s the reality we’re building at PhysicsX. But here’s the challenge: even the most advanced AI tools are worthless if they can’t integrate into the real-world workflows of engineering teams. Our customers often operate within highly mature systems, where even minor disruptions can have major consequences if not rolled out with care.
So, how do we bridge the gap between state-of-the-art technology and the status quo? At PhysicsX, we do it through Delivery.
What is Delivery?
At PhysicsX, we’re organized into four core teams:
- Product: builds and maintains the PhysicsX platform.
- Research: discovers the next big breakthrough to redefine how engineering is done.
- Delivery: embeds with customers to ensure our tools are both used and useful.
- Operations: coordinates systems and processes to make all of this possible.
Our Delivery team is made up of simulation engineers, machine learning specialists, data scientists, and software developers who embed directly into customer engineering teams. Think of them as bridge-builders: they take cutting-edge AI capabilities from our research labs and translate them into practical tools that solve real engineering problems in real-world engineering workflows.
Why is this necessary? Because every engineering organization is different, and context matters. Take aerodynamics, while the underlying physics may be the same, the way an aerospace Original Equipment Manufacturer (OEM) designs and manufactures products is fundamentally different from how a Formula 1 team optimizes car design.
We know engineering is hard, and we approach it with humility. Our customers are deep domain experts who have spent years immersing themselves in the challenges of their industries. That’s why generic tools, dropped in from the outside with the hope they’ll somehow fit, rarely end up delivering value. Instead, we embed expert teams to work shoulder-to-shoulder with our customers. Together, we build tailored solutions that help them make better, faster engineering decisions where it matters most.
How Does Delivery Work?
Engineering has its own unique flavor in every organization, and understanding what decisions are being made, what could make those decisions better, and what technology can enable that improvement isn’t a one-time task. It’s a continuous process that demands deep, ongoing engagement and a clear understanding of what truly matters to the customer.
Let’s look into a concrete example: PhysicsX partnered with an aerospace company to transform how they design hydraulic manifolds — the intricate network of channels that control fluid flow within jet engines. Traditionally, every design iteration took days of computational fluid dynamics (CFD) simulation, meaning engineers could only explore a limited number of options before deadlines pushed them to commit, leaving significant performance gains on the table.
Our Delivery team collaborated closely with their hydraulic systems experts to develop a tailored Large Physics Model (LPM) that has learned the physics of fluid flow through a training process based on the results of numerical simulation. Thanks to the diverse expertise of our embedded Delivery team, every stage of data generation and model development was significantly accelerated:
- A PhysicsX Simulation Engineer partnered closely with the customer’s subject-matter experts to adapt the physics simulations that generated the training data for the LPM.
- A customer Data Scientist collaborated with the PhysicsX Research team to train the LPM using the most suitable architecture, whether neural operators, diffusion models, variational autoencoders, or other state-of-the-art techniques.
- A PhysicsX Machine Learning Engineer collaborated with our Product team to integrate the trained model into an interface designed to be both used and useful. Working closely with the end users, the team extended the application to integrate directly into the customer’s existing tools and workflows. In this example, end users could instantly share results with colleagues via a simple weblink, replacing the previous two-day process of preparing and emailing a PowerPoint presentation.
This doesn’t just speed up the customer’s work — it transforms how they design. Engineers can now explore hundreds of variations, optimize across multiple conditions simultaneously, and push creative boundaries that were previously out of reach. The result? Design cycles that once took months are now compressed into weeks. More innovative products reach the market faster, with engineers focusing on creative problem-solving instead of waiting on simulations.
The result? Design cycles that once took months are now compressed into weeks.
Every customer brings unique challenges and goals. It’s the Delivery team’s responsibility to uncover those needs, identify the right solutions, and prioritize which valuable features to build first, ensuring that what we deliver truly makes a difference.
What Makes PX Delivery Unique and Effective?
We're builders and problem solvers, not consultants. Traditional consulting delivers reports and recommendations; we deliver working tools that are used and genuinely useful. We’re not here to wear suits and make slides (though we’ll use them when they help). One day, machine learning will be fully embedded in engineering, and our customers will use our platform independently. But until then, as the industry navigates major shifts in capability, Delivery exists to help bridge that gap by building workflows that solve our customers’ most significant problems. We’re not shouting instructions from the sidelines. We’re in the field, working side-by-side with our customers, helping them solve their hardest problems.
We work in the open, not behind closed doors. Too often, AI implementation happens in isolation — data scientists work alone, build models, and hope they’ll be adopted. Our Delivery team takes a different approach: we work transparently alongside customer engineers, sharing knowledge, scoping, and building capabilities together. This not only creates buy-in and trust, but also ensures the solutions we build truly fit into existing processes and workflows.
Delivery doesn’t happen in the background. We’re not quietly writing code, reviewing pull requests, training models, or post-processing simulations in a vacuum. We do it with our customers, collaboratively and visibly. That could mean running a quick analysis in a notebook with an engineer from the customer’s team to troubleshoot a broken test rig, or extending a simulation to a new operating condition to help them win new business. It’s about creating immediate value while laying the groundwork for long-term success.
We're connected to the cutting edge. Because our Delivery team has direct lines into PhysicsX’s Research and Product organizations, state-of-the-art capabilities can move from lab to customer in weeks, not years. When our researchers improve a new neural operator technique, Delivery can immediately apply it to real engineering problems. This tight, rapid feedback loop accelerates both scientific progress and real-world impact, creating a cycle where research feeds delivery, and delivery strengthens research.
Delivery is a critical route for feedback to get to Product and Research. With our teams embedded directly inside customer environments, the traditional design-build-test cycle is dramatically compressed. New features can be shipped, tested, and iterated in front of real users within days. For a company like PhysicsX, that kind of iteration speed is a key lever in our ability to execute on our mission.
The relationship between Delivery and the rest of PhysicsX is deeply collaborative. Delivery teams operate on the frontlines, bringing back insights that sharpen our research and product directions. At the same time, Product and Research actively engage with Delivery, applying cutting-edge tools like our Large Geometry Models in real projects to refine their performance, and iterating on features alongside customers to ensure they land effectively. This two-way workflow keeps us agile, aligned, and always advancing.
We focus on what's important, not just what's interesting. Every tool, model, and integration we build is guided by a single question: will engineers actually use this to make better decisions? That focus keeps us grounded in what really matters to our customers. It ensures we’re solving high-value, real-world problems instead of just building things that are technically impressive. This customer-centric mindset is at the heart of everything we do, and it's what makes our value meaningful, impactful, and lasting.
Delivery builds context. At PhysicsX, we’re not just building an AI-native platform for engineering and manufacturing. We’re also building a world-class, ambitious, interdisciplinary team capable of driving innovation at the scale our customers demand. To do that, our people need to be learning and evolving constantly. Delivery is one of the fastest ways to make that happen. It’s where colleagues from across the company spend time to deeply understand customer needs and learn how to build things that are genuinely useful.
Solving hard problems for advanced engineering organizations requires more than just technical know-how. It demands a deep understanding of how these companies operate — how decisions are made, where the real bottlenecks lie, and how tools can truly fit into the rhythm of day-to-day work. Delivery gives our team that exposure. It’s a place to immerse in industry contexts and bring those insights back to research, product, and platform development functions.
Everyone in Delivery comes from a technical background in physics, simulation, machine learning, software, and more. But technical skill alone isn’t enough. We know that doing great work also means making it understandable and clearly valuable. That means engaging with customers, communicating complex ideas in clear terms, and showing why what we’re building matters. Delivery is where technical people come to build real solutions, and to tell the story of why they matter.
Changing How Engineering Is Done
The future of engineering isn’t just about better AI — it’s about AI that fits naturally into the way engineers think, decide, and create. Our Delivery team is how we make sure that this future doesn’t stay distant or abstract.
Every person in Delivery at PhysicsX operates with one guiding question: will this get used?
“This” might be a model, an application, some software, a technique, or even just a conceptual framework. It’s not enough to build things that are novel, so we focus on building things that are both new and necessary.
Delivery is the tip of the spear, where the latest physical AI capabilities are applied to mission-critical engineering problems that truly matter. And it’s how we ensure that the transformative potential of AI isn’t just imagined, but implemented, adopted, and realized.
Think you’re up to the challenge? We’re hiring in Delivery roles in North America and Europe. Find out more here.