Leadership
Building AI organizations that do good work
My career has moved from control systems research and robotics engineering into building AI organizations and shaping Physical AI products. I connect product strategy and technical direction with delivery and coaching. At Nebius, I help robotics teams put data collection, training, simulation, evaluation, and field feedback into a working Physical AI flywheel, including work on Physical AI Workbench and serverless simulation and training proofs of concept.
What I lead
01
AI strategy and portfolio direction
Find the opportunities where ML can genuinely help, sequence the work, and make the tradeoffs easy for leaders to see.
Go/No-Go Decisions for Machine Learning Products →02
AI product development & perception
Guide perception systems from data and evaluation through product integration for inspection, robotics, and autonomy work.
Plan the Path From Prototype to Product →03
Physical AI product delivery
Bring data collection, simulation, evaluation, field feedback, and deployment into one learning loop.
Why Physical AI Teams Become Infrastructure Companies →04
Team building and operating cadence
Hire and coach multidisciplinary teams, make ownership clear, and build a healthy rhythm for planning and learning.
05
Data, evaluation, and ML platforms
Build the foundations that help a team understand model behavior, run systems responsibly, and improve them over time.
Evaluate the Decision, Not Just the Model →06
Aligning leaders and customers
Turn technical constraints into clear choices for customers and leaders while keeping delivery teams close to the problem.
Define the Problem Before the Model →
How I like to work
01
Start with the outcome
Start with the customer decision, the value it could create, and what it costs when we get it wrong.
02
Build the evidence loop
Treat data quality, representative evaluation, and feedback from the field as part of the product.
03
Organize around delivery
Give product, ML, platform, and domain partners clear ownership and a shared way to make decisions.
04
Release deliberately
Launch in a controlled way, keep people in the loop where it matters, and watch what the product does for customers.
05
Learn and communicate
Talk honestly about what we are seeing, adjust the roadmap, and help leaders understand what comes next.
A few examples
Case study 01
Building Physical AI infrastructure with NVIDIA
Nebius | Field CTO, Physical AI & Robotics
- Challenge
- Give robotics teams one workflow for data collection, simulation, and evaluation instead of a pile of disconnected tools.
- Leadership
- Co-lead the Physical AI team, contributed to Physical AI Workbench and serverless simulation and training proofs of concept, and co-led a joint demo with NVIDIA and Black Forest Labs at GTC Berlin.
- Scale
- 27+ structured customer interviews shaping three solutions built on Isaac Sim, Isaac Lab, GR00T, and Cosmos.
- Outcome
- An open-source workflow that connects data, simulation, and evaluation into one learning loop for robotics teams.
Case study 02
Building an applied AI organization
Zeitview | Director of AI/ML
- Challenge
- Establish an AI organization and operating model that could support multiple product verticals.
- Leadership
- Built a 13-person department, hired AI and data-labeling leads, and set hiring priorities, product and technical strategy, roadmap, and team budget.
- Scale
- A 13-person department spanning data labeling, ML science, ML engineering, and autonomy, working with full-stack teams across five inspection verticals.
- Evaluation
- For a deployed high-severity defect alerting system, evaluated missed defects, false alerts, human review, and field tests against release targets of human-level detection and fewer than one false positive per mission.
- Outcome
- Improved analysis quality by 29%, helping attract customers and grow revenue, while cutting operational costs by 27%. Wind and solar analysis reached roughly 90% full automation, cutting their costs by over 95% and customer turnaround from about two weeks to roughly one hour.
Case study 03
Commissioning vision-guided robotic picking
Plus One Robotics | Senior Machine Learning Engineer
- Challenge
- Bring computer vision into robotic picking cells that had to work at customer sites.
- Leadership
- Owned integration end to end, including software setup, tuning, and delivery testing; shared final sign-off with project partners.
- Scale
- Commissioned picking cells for four major logistics clients.
- Outcome
- Increased picks per minute by 18% to meet a customer target, helping win contracts against competing systems.
Case study 04
Scaling a real-time analytics product
GammaEdge | Co-Founder
- Challenge
- Create and operate an analytics platform that customers could use at meaningful daily volume.
- Leadership
- Co-founded the platform and grew the product alongside its technical and operational foundations.
- Scale
- More than 200,000 daily requests.
- Outcome
- Serves more than 200,000 daily requests.