Working Student
AVL Software and Functions
- Continuing model-based control development responsibilities part-time alongside my master's studies.
Available for remote engagements · Worldwide
Hi, I'm Oguzhan —
I build safe, intelligent control systems for robots.
Robotics & Control Engineer and M.Sc. Mechatronics & Robotics at TUM. I take on remote engineering engagements across model-based control, reinforcement learning, and embodied AI — as an independent engineer, on a clean B2B basis with no payroll or employer overhead on your side.
How I work · Engagement Model
Senior engineering through a clean B2B engagement — no payroll, no employer contributions, no permanent fixed cost — on a structure Turkish law explicitly supports.
No employer overhead
You pay for the work — not the social charges, benefits, paid leave, or severance layered on a local salary.
Senior level, competitive rate
Türkiye's cost base and tax framework let me deliver senior-grade engineering well below a comparable local hire's total cost.
You pay for output
Scoped, deliverable-based B2B billing. You commit to a project, not a permanent headcount.
Legitimate & contract-backed
A registered business with its own tax identity. Scope, NDA, and IP transfer set out in a written services agreement.
01 — About
Engineer at the intersection of control theory and learning
I'm a robotics and control engineer with 2.5+ years of industry experience developing model-based control software for electric vehicles, hybrid systems, and autonomous platforms — built to ASPICE and MISRA standards at AVL.
Alongside my master's at TUM, my research focuses on making autonomous systems both capable and provably safe: combining reinforcement learning with Control Barrier Functions for quadrotor recovery, and exploring vision-language-action models for humanoid manipulation. I enjoy taking ideas from theory and simulation all the way to real hardware.
I now work as an independent engineer available for remote engagements worldwide in robotics, control, and machine learning — engaged on a clean B2B basis rather than as an employee. See how I work →
- 2.5+Years industry experience
- 1Patent contribution
- 6+Robotics projects
- C1English (IELTS 7.0)
02 — Experience
Work experience
Control Development Engineer
AVL
- Developed model-based control software for electric vehicles, hybrid systems, autonomous robotics, and e-mobility platforms.
- Built application-layer software in MATLAB/Simulink compliant with ASPICE and MISRA standards.
- Designed and maintained safety-critical control functions and algorithms — protection logic, thermal management, contactor control, diagnostics, and vehicle communication.
- Executed Model-in-the-Loop (MiL) and Software-in-the-Loop (SiL) testing and supported HiL integration and validation.
- Managed component-level requirements and tests; helped define and improve modular, reusable software architecture.
- Built Python tooling for model calibration and prototyped deep-learning algorithms for state estimation.
- Collaborated with cross-functional, multi-disciplinary teams across AVL locations worldwide — a fully distributed, remote-first way of working.
Working Student
Siemens
- Supported after-sales operations and customer incident resolution.
- Troubleshot non-conformities in medium- and low-voltage switchgear (electrical & mechanical).
- Coordinated stakeholders and subject-matter experts across operations; managed procurement, dispatch, and customs of critical materials.
- Tracked and reported after-sales non-conformities to improve quality requirements.
03 — Research
Research experience
Master Thesis Student
Autonomous Vehicle Systems Lab, TUM
- Investigating Vision-Language-Action (VLA) models for mobile manipulation on the Unitree G1 humanoid and a multi-DOF robotic hand.
- Collecting and processing real-world manipulation datasets for policy adaptation and fine-tuning.
- Developing and evaluating vision-language-conditioned manipulation policies for real hardware.
- Exploring generative policy learning — flow matching and diffusion-based methods — for embodied AI and control.
Semester Thesis Student
Autonomous Aerial Systems Lab, TUM Code
- Built a safety-critical RL framework combining PPO with Control Barrier Functions for safe quadrotor recovery and stabilization.
- Derived nonlinear quadrotor dynamics, Lie-derivative-based CBF constraints, and real-time safety-filtering pipelines.
- Designed curriculum learning and reward shaping (dual-scale Cauchy rewards with goal-directed velocity objectives) to improve recovery from arbitrary initial conditions.
- Added domain randomization, parameter-uncertainty modeling, and calibrated disturbance injection for robustness and sim-to-real transfer.
- Extended Flightmare with Stable-Baselines3 integration and disturbance models; built an NMPC baseline for comparison.
- Deployed the learned policy on a real Agilicious quadrotor — robust recovery from fully inverted states; results contributed to a paper submitted to an international controls conference.
04 — Projects
Selected projects
Foundation-Model Autonomous Driving on F1TENTH
Full ROS 2 autonomous-driving stack on NVIDIA Jetson Nano integrating perception, localization, planning, and control. Combined VLMs and LLMs for high-level decisions with a Stanley controller for lane tracking, guarded by a finite-state-machine safety layer. Validated end-to-end on real hardware.
View on GitHubAutonomous UAV Cave Exploration & Navigation
ROS 2 quadrotor for 3D cave exploration in Unity. OctoMap occupancy mapping, frontier-based exploration, sampling-based motion planning, and a geometric SE(3) controller for aggressive 3D trajectory tracking.
View on GitHubMobile Robot for Intralogistics 1st place
Raspberry Pi LEGO robot for warehouse navigation. Dijkstra global planning, OpenCV perception, PID motor control for differential drive, and MQTT-based task allocation — won 1st place in the course challenge.
Humanoid Gait Imitation with RL
MuJoCo RL framework for humanoid locomotion using OpenSim inverse kinematics from motion-capture data for imitation learning, with reward shaping for stable, physically plausible gait.
Embedded Ball-and-Plate PD Control
Real-time PD control on a dsPIC33F for closed-loop circular trajectory tracking. Low-level embedded software using registers, interrupts, ADC sensing, and PWM dual-servo actuation — validated on real hardware.
Patent — Hybrid Modeling & Control Method
Co-invented a framework that combines physics-based modeling with data-driven prediction and control, blending first-principles dynamics with machine learning for more accurate, robust real-time control.
05 — Skills & Education
What I work with
Technical skills
- Python
- MATLAB & Simulink
- C++
- Object-Oriented Programming
- ROS / ROS 2
- Linux
- Git
- PTC Windchill
- Codebeamer
Domains
- Model-Based Control
- Reinforcement Learning
- Control Barrier Functions
- NMPC
- Safety-Critical Systems
- Embodied AI
- Embedded Systems
- ASPICE / MISRA
Languages
- English — C1 (IELTS 7.0)
- Turkish — Native
Education
Technical University of Munich
2024 — 2026M.Sc. Mechatronics and Robotics · Munich, Germany
Yıldız Technical University
2018 — 2022B.Sc. Electrical Engineering · Istanbul, Turkey
GPA 3.57/4.0 (German equivalent 1.6)
06 — Remote work
How I work remotely
I've spent years collaborating across globally distributed teams, and I'm set up to be effective from day one in a remote-first engagement — wherever your team is.
Distributed by default
At AVL I worked daily with cross-functional teams across multiple countries and sites — async collaboration and remote delivery are how I'm used to operating.
Flexible hours & overlap
Based in Munich (CET). Happy to flex my schedule to keep solid daily overlap with teams across Europe, North America, and beyond.
Clear async communication
I write things down — concise updates, documentation, and well-scoped tickets — so progress stays visible and decisions are easy to follow without constant meetings.
Self-directed delivery
Across industry and research I've taken projects from ambiguity to working hardware independently — comfortable owning outcomes with minimal supervision.
07 — Contact
Let's scope your next engagement.
I take on remote engineering engagements in robotics, control, and machine learning. Tell me about the project and I'll come back with a clear scope, deliverables, and a rate. The fastest way to reach me is email.