Own the product roadmap and delivery for nTop's distributed computing capabilities. Lead discovery and build the first integration with MDO and cloud/HPC job systems, define headless execution UX, instrument usage metrics, and work with field engineers and GTM teams to validate requirements and drive adoption.
About nTop
The Role
What You'll Do
What Success Looks Like
Who You Are
Required
Preferred
Why This Role
nTop builds parametric design software for the hardest geometry problems in aerospace, defense, and industrial turbomachinery. Our platform lets engineers define a design as a parametric program — a model that represents not one aircraft or turbine, but every variant of it that a program might need to consider. Customers use nTop to compress the journey from requirements to fielded systems, replacing years of iterative hand-modeling with systematic exploration across design spaces that were previously too large to search.
The next step in that journey is scale. The unit of work is no longer an individual engineer at a screen — it's large-scale distributed computing: thousands of design variants evaluated in parallel, across cloud, HPC, and on-prem infrastructure, with results feeding directly into AI-driven optimization and generative design workflows. We're building the product that makes that possible.
We're looking for a Product Manager to own the problem of how nTop customers run and scale large-scale distributed computing — defining, launching, monitoring, and collecting results from thousands of design variants evaluated in parallel — and to turn that understanding into a prioritized roadmap for where nTop should build first.
This is a greenfield product area. You'll determine what first-party distributed computing capability looks like, and scope and deliver nTop's first integration with the tools our customers already use (HEEDS, ModelCenter, PhysicsX Flux, and others). The concurrent-agent pricing model — a new revenue stream for nTop — is launching on a separate track ahead of this role; your job is to understand how customers plan and budget for compute-intensive work so the product and packaging experience holds up as that model rolls out. The north-star metric for this team is headless nTop notebook executions: customers exploring more design variations, in less wall-clock time, without a human in the loop for every run.
Own the roadmap. Define and maintain the product roadmap for distributed computing. Prioritize against customer value, technical feasibility, and business impact — and defend those priorities with clear, data-driven rationale to engineering, leadership, and go-to-market teams.
Build the first integration and define the native experience. Lead discovery across the distributed computing platforms customers already rely on — HEEDS, ModelCenter, PhysicsX Flux, and cloud-native job services from AWS and GCP — and, based on that discovery, scope and deliver nTop's first integration with the highest-value platform, deferring the rest explicitly for a later phase. Own the build vs. partner decision for that integration. Shape the first-party headless execution experience — job definition, submission, monitoring, failure handling, and results collection across workstation, on-prem HPC cluster, and cloud — for the workflows this integration unlocks.
Understand usage and consumption patterns. The shift to concurrent-agent pricing is already in motion and will launch ahead of this role. Your job is to understand how customers actually plan, budget for, and scale their usage of compute-intensive workloads, and feed that insight into the ongoing pricing and packaging decisions owned by product leadership.
Measure and instrument. Define the metrics — headless executions, concurrent job volume, time-to-result, integration adoption — and drive the instrumentation needed to track them. Use that data to prioritize and to tell the story of progress to the business.
Work the field. Partner with forward-deployed field engineers and engage directly with advanced users to collect requirements from live workflows, stress-test your roadmap against real constraints (IT governance, security, export control, compute cost), and close the loop between production use and product priorities.
Collaborate across design, UX research, and go-to-market. Work with design and UX research to ensure that scale doesn't come at the cost of workflow clarity. Partner with Marketing, Sales, and CS to translate distributed computing into value that resonates with both engineering teams and the program-level stakeholders who expand usage.
90 days: You have a validated picture of how advanced users run large-scale exploration workflows today, where the friction is, and which integration and first-party opportunities are highest value. You've proposed a prioritized roadmap and gotten alignment on it.
2 quarters: Your first integration (e.g., HEEDS, ModelCenter, or a cloud-native job scheduler) is live with early users. Headless execution volume is measurable and growing, and you have a clear, data-backed point of view on how customers are adopting the concurrent-agent model.
Required
- 4–7 years of PM experience in complex, technical B2B software, with ownership of a product area spanning both user-facing workflows and infrastructure or platform concerns.
- Experience shipping enterprise software across cloud, on-prem, or customer-managed environments, with working knowledge of modern compute and distributed computing models — containerization, Kubernetes, HPC job schedulers, or cloud-native job execution.
- Product-level understanding of scalability, reliability, and security in compute-intensive systems — enough to reason credibly about architectural tradeoffs and work effectively with the engineering teams building the infrastructure.
- Strong data orientation: able to define instrumentation requirements, establish leading indicators, and use quantitative evidence to prioritize and communicate.
Preferred
- Experience with MDO, design space exploration, or engineering orchestration tools (HEEDS, ModelCenter, OpenMDAO, Dakota, PhysicsX Flux, or similar).
- Exposure to usage-based or consumption-based pricing models, or experience transitioning a product from seat-based to compute-based licensing.
- Background in aerospace, defense, or industrial engineering software, or experience selling into engineering-heavy organizations with complex procurement and IT governance requirements.
The shift from individual-engineer CAD to large-scale parallel computation is one of the most consequential transitions happening in engineering software right now. This role sits close to the center of it — owning how nTop customers will run their first large-scale distributed computing workflows, delivering the integration that proves out the model, and building for customers whose work is genuinely high-stakes. The problems are hard, the ownership is real, and the potential is significant.
nTop is an equal opportunity employer committed to building a diverse and inclusive team.
CompensationThe base pay range for this role is $175,000 – $205,000 per year.
Please note that nTop may use AI products to assist in the recruiting process. All resumes are reviewed by, and all employment decisions are made by, nTop's Hiring Team.
If you require a reasonable accommodation during the interview process, please don't hesitate to email us directly.
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