Self Employed

My Services: 
👉 Configuration Management (SUP.8)
👉 Product Release (SPL.2)
👉 ASPICE process consulting and internal assessments, gap analysis

I‘m supporting automotive suppliers, startups, OEMs and established Tier1s in understanding and tackling the challenges that come with developing, producing, certifying and implementing next generation automotive L3+ (ASIL B+) products such as highly autonomous driving platforms, lidar, radar, camera and HPC systems

Software Quality Process Engineer

scaletech

Check out the scaletech company page ✌🏼

+3M monthly tech content impressions 😮‍💨

Owner and Content Creator

The Lidar Society

👉 Crafting unique engineering grade content that turns heads
👉 Managing the Lidar Society, which is a community (currently LinekdIn Group) of professionals, investors, and enthusiasts working in the field of lidar technology. 
👉 It is a platform for knowledge-sharing, collaboration, and networking among members, with a focus on advancing the field of lidar technology and shaping the future of autonomous transportation. 
👉 Joining the community offers access to expert insights, opportunities to connect with industry leaders and other like-minded professionals, and a chance to be part of a group driving innovation in the field.

Automotive B2B Content Creator (building "The Lidar Society")

Ibeo Automotive Systems GmbH

Developing the first serial solid state LiDAR sensor for automotive and industrial applications.
Responsible for Release Management and Production Release of the IbeoNEXT LiDAR sensor going from Prototype to SOP.
* Planning of all Sensor Releases with Product, Application and Production Management 
* Handling all Configuration items along the way
* Responsible for on-time, quality release and documentation of the ibeo Next 4D solid state Lidar sensor

Release & Configuration Manager LIDAR Sensor

Supported the current RTE in daily tasks and took over IT, Project Management/Scheduling and Organizational tasks.
Onboarding time on the ibeoNext Project until I eventually took over Release Management for the project.

Release Train Engineer Lidar Sensor

Development, Planning and Implementation of Culture Space.

Coroporate Culture

Customer acquisition, presentations, project management, strategy and planning.
* Prepared customer presentations for Group Lead
* Supported customer and project acquisition.
* Responsible for L4 AD use case development and evaluation.
* Project Planning and Controlling of research funded AD projects.

Project Manager Autonomous Driving

Responsible for the Vehicle and Sensor Simulation activities and Virtual Testing.
Supported the founding of subsidiary ibeo-nl.de in Eindhoven, NL.
* Initiated the ibeo simulation activities with newly founded company.
* Project lead at ibeo for vehicle simulation and implementation of ray tracing/casting based Lidar sensor models.
* Counterpart to ibeo-nl CEO for all sorts of administrative and project related topics. 
* Goal of the project was to create a sophisticated simulation suite for high quality physics based Lidar sensor models, to create synthetic data for environmental perception model evaluation.

Project Manager Simulation

Simulation framework development and project management for Lidar sensor model development.
Integrated ibeo, IPG Carmaker and Vires VTD SDKs, to create data for environmental perception algorithm development.

Software Developer - Virtual Testing

Continental

Vehicle Simulation Engineer in the Virtual Vehicle Test department. 
IPG Carmaker was facilitated as a virtual integration platform for all kinds of prototype software as well as self developed models. Just to name a few interfaces: Matlab/Simulink, Dymola(Modelica), C, DLL etc. Fully compatible with FMI Standard.
Worked on SIL, MIL and HIL projects as well as supported road testing. Also a framework for automated testing was developed.

Virtual Test Engineer

HWA AG / Mercedes-AMG DTM Team

Intern in Mercedes-AMG DTM Team's Vehicle Dynamics Group

Vehicle Dynamics Engineer

MAHLE Powertrain Ltd.

Torque vectoring controller design to generate desired handling perception of TtR electric hybrid improving fun-to-drive aspect. MIL based performance and robustness validation followed by final implementation and calibration on an actual prototype vehicle. Particular focus on formulating general handling statement for different driving modes and designing sophisticated desired handling generator.

Research Intern

Institute of Automotive Engineering TU Braunschweig

ACC development
longitudinal vehicle dynamics
processing of measured data
developing vehicle models
vehicle dynamics validation

Graduate Research Assistant

IPG Automotive GmbH

Bachelor Thesis

Parameterisation and validation of a Formula Student racecar using on track measured data.

Parametrierung und Validierung eines Formula Student Fahrzeugmodells anhand von Messdaten aus realen Testfahrten.

Research Internship

Transcoject GmbH

IBS, University of Applied Sciences Kiel

Industriebegleitetes Studium (IBS) Fachhochschule Kiel

Integrated Degree Student

You are curious about Lidar? Last 12 Months +1.9M Automotive Experts joined us | Our lidar community is fueled with genuine lidar insights. You won’t find anywhere else | This is your chance to get 1:1 with Lidar Leaders

Everyone wants to match China speed or Tesla agility, but few automotive teams know how to get there. 

The usual suspects—poor developer experience, ASPICE bottlenecks, and cultural misalignment—are holding your team back.

Your developers are overwhelmed. 

Quality managers are frustrated. 

And despite your efforts with Agile-at-Scale and ASPICE consultancies, nothing seems to click.

Here’s the good news: the secret to China speed is simple but not easy. 

With a proven approach, I help automotive software teams achieve happy developers, happy quality managers, and hassle-free ASPICE compliance—all while keeping your existing processes and tooling intact.

I’ve spent the past decade solving these challenges. 

-> I established configuration and release management systems for a startup that became a Tier 1 lidar sensor supplier

-> Isupported a Silicon Valley startup in implementing an ASPICE-compliant process landscape (ensuring developers stayed in flow without being burdened by excessive manual effort)

-> I worked with a major German premium OEM to rethink their software development journey.

Together, we stopped wasting time on nonsense like manual compliance in Excel, Confluence pages, and excessive Jira templates. 

Instead, we focused on automated compliance monitoring, seamless reporting, and developer-level training—allowing teams to live up to their full potential.

If you’re ready to stop spinning your wheels and finally outpace the competition, let’s talk. 

Limited slots available—message me to learn how.


Porsche’s latest numbers are brutal. 😳
This might’ve cost someone’s job.

Last year Jan–Sep: €4.0B operating profit.
This year: €40M.

That is not a “soft year.”
That is 99% wiped out.

Return on sales: 14.1% → 0.2%.
A premium brand posting rounding-error margins.

And Q3?
Almost €1B lost in one quarter.

Why?

Not one thing.
A stack of technical and business failures hitting at once:

• EV overdrive. Billions sunk into a plan the market didn’t want.
• €3B restructuring hit to unwind that plan.
• China down ~25%. Porsche’s most important market just… stopped buying.
• US tariffs tearing margin out of every car shipped.
• Product delays. Taycan and electric Macan not pulling their weight.
• The brand stuck between regulation and reality:
customers still want ICE, policy says EV, and Porsche tried to please both.

Investors say “2026 will be better.”
Sure. Forecasts always look clean on PowerPoint.

But here is the uncomfortable part nobody likes to say aloud:

If Porsche can’t make the EV transition financially stable,
who in Germany’s auto industry can?

This is not a dip.
This is the warning signal.

And it’s the outing of former Porsche CEO Blume.

New CEO will be former McLaren Boss Michael Leiters.

Lukas Timm

Stop losing deals to competitors with weaker tech but stronger visibility. You already have the expertise — you just need a system to make it seen.

No alternative text description for this image

MASSIVE. 🤯

What’s presumably the world’s first self-driving DDOS attack — and Waymo handled it seamlessly.

This is what happened:
50 people went to San Francisco’s longest dead-end street.
All ordered a Waymo at the same time.
Dozens of robotaxis showed up.
No one got in.

After ten minutes they charged a $5 no-show fee and left.
Waymo then disabled all rides in a two-block radius until morning.

Handled without humans.
No crashes.
No chaos.

Larger implications:
Should these people be fined for reckless behavior?
Or should Waymo write them a check for free large-scale edge-case testing?

Either way — looks like self-driving robotaxis are ready for prime time.

————————

Tech Founder authority content by scaletech

Talks about #lidar, #automotive, #perception, #computervision, and #autonomousdriving

Krzysztof Miksa

Full self-driving isn’t years away because the technology isn’t ready.

It’s years away because we’re solving 10 interdependent hard problems simultaneously.

Most people think autonomous vehicles are a perception problem. Add better cameras, train bigger models, collect more data, done.

The reality is far more complex.

Why Level 4 autonomy remains engineering’s hardest challenge

The long tail problem: Billions of rare edge cases that simulations cannot capture.

Multimodal perception: Fusing sensors while maintaining superhuman accuracy during occlusions, weather, and construction zones.

Behavioral prediction: Forecasting human trajectories as a game-theoretic problem requiring billions of real-world miles.

Real-time decisions: Computing safe actions under 10ms latency constraints.

SOTIF validation: Safeguarding against unknown unknowns across all operational design domains.

Hardware constraints: Deploying billion-parameter models on cost-effective embedded systems.

Functional safety: Preventing single-point failures per ASIL-D ISO 26262 standards.

The mapping dilemma: HD maps enable robustness but limit scalability.

Regulatory fragmentation: Navigating conflicting frameworks across markets.

Economic reality: $200,000+ sensor suites, 10+ million training miles, $50+ billion in cumulative R&D since 2015.

The tradeoffs at scale

Tesla sacrifices redundancy for cost efficiency and global scalability.

Waymo and Nio deploy expensive multi-sensor suites for robust but geographically limited performance.

Wayve and Xiaomi balance performance against real-time constraints.

No single approach dominates. Each reveals different bottlenecks.

There is no silver bullet. Only sustained multidisciplinary innovation across perception, planning, validation, hardware, safety, and regulatory domains.

That’s what makes autonomous vehicles one of the hardest engineering challenges in history.

Self driving uncovered 

„I won’t explain everyone on this project ASPICE. They should first take a training.“

That‘s what I heard the Quality managers say when we discussed how to unf*** the project.

3-4 years in, 18 months till SOP, just failed the ASPICE assessment miserably.

No L3, no L2, no L1 - level 0 🫣

Key findings where the usual suspects:

- inconsistent config baselines 
- requirements not traced to implementation
- tests not traced to reqs 
- detailed design missing
- overall a lot of confusion across interviews engineers what the assessor was asking for

„What’s a review checklist?“
„What’s amendment history?“
„What’s a change record?“

Engineers show jira tickets, ticket change history, cicd logs for test results…

Release baseline? Here you go, git release tag…

No. 

But nothing was good enough.

Requirements HAD to be in PTC Integrity not Jira (cause Jira is not ASPICE compliant 🤯)

Test results needed to be presented in docx test reports.

Release baseline? Where’s the docx release report with amendment history and quality sign off?


So the result was:

Management is mad.

Engineers are frustrated.

Quality: „we told you“


This still gets my blood boiling.

The function of quality should be making sure engineers have the environment to perform in.

Quality should steer DevOps in building the infrastructure that „enforces“ ASPICE (as much automated and seamless as possible) so engineers work „compliant“ by definition.


But the Quality I’ve met the last 10 years across countless tier1s and OEMs was arrogant and finger pointing.

„They (the engineers) just want to „build“, are too lazy to document properly.“

„We’ve defined the process, it’s their fault they’re not following.“


If +80% of your engineering org works „around“ your processes.

It’s a process and tools problem.

It’s YOUR problem (you = quality).


Quality‘s customers are the engineers. 

Make your customer happy.

Build processes methods and choose tools that make your engineers happy.

Is Tesla burning too much money? 😳 

Net income dropped 37% to $1.37 billion. 
Operating income fell 40% to $1.6 billion. 
Operating margin compressed to just 5.8%.

Meanwhile revenue hit a record $28.1 billion. 
Deliveries reached an all-time high of 497,099 vehicles.

The paradox resolves when you look at operating expenses.
Up 50% year over year.

The Reason? 
↳ AI infrastructure 
↳ Autonomy R&D 
↳ Energy storage expansion

Tesla closed the quarter with $41.6 billion in cash. 
Their highest balance ever. 

Free cash flow surged to $4 billion in Q3 alone.
This isn't a liquidity crisis.

It's what happens when a company deliberately trades margin for capability.

Most automotive companies optimize for quarterly profit.

- Tesla is scaling compute to 81,000 H100 GPU equivalents. 
- Building first-generation production lines for humanoid robots. 
- Expanding Robotaxi to 8-10 metro areas by year end.

Their energy storage business grew 44% to $3.4 billion.

While automotive revenue inched up 6%, the infrastructure play is accelerating.

For tech leaders watching this space:

The traditional automotive playbook doesn't apply here.

Tesla is burning margin on purpose. 
Reallocating profit from hardware sales into AI training capacity and autonomy deployment.

Whether this strategy pays off depends entirely on whether their FSD actually achieves autonomy at scale.

But the financial structure is clear.

They're not running out of cash.

They're investing it before someone else does.

------

PS: I'm helping B2B tech founders go from invisible -> omnipresent driving real results. If you are on the fence to start building your personal founder brand on LinkedIn let's talk!

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