Jonathan Lwowski

About

• AI & Robotics Strategy
• Product & Roadmap Ownership
• Building & Running AI Teams
• Computer Vision & Perception
• Physical AI Workflows
• ML Platform & MLOps
• Customer Discovery
• Executive Stakeholder Engagement

A few examples

A few public examples of the team, product, and technical work I have led.

1. Case study 01

   Building an applied AI organization

   Zeitview | Director of AI/ML

   Establish an AI organization and operating model that could support multiple product verticals.

   Created a multidisciplinary foundation for delivering AI capabilities across five business verticals.

2. Case study 02

   Turning inspection data into intelligence

   Kespry and Zeitview | Computer Vision and ML Leadership

   Move computer vision work beyond prototypes and into inspection workflows with real delivery constraints.

   Built experience delivering inspection intelligence for residential and industrial use cases.

3. Case study 03

   Scaling a real-time analytics product

   GammaEdge | Co-Founder

   Create and operate an analytics platform that customers could use at meaningful daily volume.

   Serves more than 200,000 daily requests.

How I work

For me, building a useful ML product is less about rushing to train a model and more about making good decisions together. I like to start with the real problem, be honest about the constraints, and give the team a clear next step.

1. 01

   Start with a problem people actually feel

   I begin with the customer decision and the outcome that needs to improve. When the value, user, or measure of success is still fuzzy, it is usually time to learn more instead of building a model.

2. 02

   Be honest about the data

   I look closely at what data is available, how representative it is, and what it will take to label, govern, and maintain it. Those realities should shape the plan from day one.

3. 03

   Agree on what good looks like

   Before optimizing anything, I want the team to agree on what good looks like. A useful evaluation connects model behavior to a real decision, workflow, or customer outcome.

4. 04

   Keep the first version small and useful

   Use the simplest model and integration that can test the product bet. It is much easier to plan for ownership, human intervention, monitoring, and iteration while the work is still small enough to change.

5. 05

   Decide what to do next together

   Bring product, engineering, data, and operations into the conversation. The answer might be invest, narrow the scope, learn more, or choose a simpler solution. A clear answer helps everyone move forward.

Things I have learned about leading teams, making product decisions, and building AI systems that have to work in the real world.

1. Sep 2025 · 2 min read

   Why Physical AI Teams Become Infrastructure Companies

   Product strategy for robotics must account for the data, simulation, evaluation, and compute systems that make dependable physical intelligence possible.

   Physical AI · Product Strategy · AI Infrastructure

2. Apr 2024 · 2 min read

   Go/No-Go Decisions for Machine Learning Products

   A practical decision framework for choosing whether an ML initiative has enough evidence, data, and delivery readiness to advance.

   Machine Learning · Product Management · Strategy

3. Apr 2024 · 2 min read

   Estimate ML Effort Before You Commit

   Estimate the work, risk, and likelihood of success across the full ML product lifecycle before setting expectations.

   Machine Learning · Planning · Leadership

Publications

Experience

1. Jun 2026 to Present

   Field CTO · Physical AI & Robotics

   Nebius

   Co-leading Physical AI across product strategy, engineering, customer discovery, and field delivery for a robotics infrastructure platform.

2. Sep 2021 to Jun 2026

   Director of AI/ML

   Zeitview

   Grew the AI department from zero to 13 people, established its operating model, and owned AI product, engineering, perception, and autonomy strategy across five business verticals.

3. May 2021 to Present

   Co-Founder

   GammaEdge LLC

   Founded and grew a real-time options analytics platform serving more than 200,000 daily requests.

4. Aug 2025 to Present

   Strategic AI Advisor

   Strike Coach

   Advising leadership on AI strategy and scalable growth.

5. Jan 2020 to Aug 2021

   Lead Machine Learning Engineer

   Kespry

   Led machine-learning platform and computer-vision development for residential and industrial inspection.

6. Jul 2019 to Jan 2020

   Senior Data Scientist

   Booz Allen Hamilton

   Conducted applied research in image steganography detection, purification, and adversarial robustness.

7. May 2018 to Jun 2019

   Artificial Intelligence Developer

   Plus One Robotics

   Led computer-vision integration for robotic manipulation, including pick-point detection and place verification.

8. Feb 2013 to May 2019

   Undergraduate & Graduate Research Assistant

   The University of Texas at San Antonio

   Conducted robotics and controls research in UAV and UGV obstacle avoidance, multi-robot formation control, and cloud robotic networks.