Clients & Collaborators

Companies I've built real things with.

From early-stage robotics startups to established industrial players, I've been trusted to take on the hardest parts of engineering problems — the parts that don't have a tutorial.

18+
Client organisations
6
Countries
92%
Return & referral rate
Helios Robotics
Robotics · Berlin
Pulse Autonomy
AV Systems · Munich
Atlas Technologies
Manufacturing · Istanbul
Orion FinTech
Finance · Amsterdam
ETH RSL
Research · Zürich
NovaMed
MedTech · Istanbul
Vektor Energy
Energy · Ankara
Stratos Labs
AI Research · London
Selected Work

How I've worked with these organisations

Three client engagements that represent the depth and variety of what I take on — and what it looks like when engineering projects go the way they should.

Helios Robotics · Berlin, Germany · 2022–2024

Building the ML Team & Vision System for a 6-DOF Industrial Arm

Helios came to me with a prototype robotic arm, a vision system that worked in the lab, and a hard deadline for a pilot deployment with a German automotive supplier. Over two years as their Lead ML Engineer, I built the perception stack from scratch, scaled the team, and took the system from lab bench to six production facilities. The core challenge was making the grasp pipeline robust enough to handle the full variance of real assembly-line conditions — variable lighting, worn parts, and operators who don't want to recalibrate.

94%
Grasp success rate
6
Facilities deployed
8×
ML team growth
Full case study
Pulse Autonomy · Munich, Germany · 2020–2022

L4 Perception Stack: Sensor Fusion Under Adverse Weather

Pulse's autonomous vehicle platform was performing well on sunny test tracks and struggling in rain, fog, and low-light urban conditions. My focus was multi-modal sensor fusion — combining camera, LiDAR, and radar outputs with a learned fusion model trained on domain-randomised simulation data and fine-tuned on real-world edge cases. The key insight was treating sensor degradation itself as a learned signal, not a failure mode to mask out.

28ms
Inference latency
+41%
Rainy-weather mAP
3×
Faster retraining cycle
Full case study
Vektor Energy · Ankara, Turkey · 2025

Demand Forecasting for Grid-Scale Energy Management

Vektor operates energy distribution infrastructure across five regions of Turkey. Their existing forecasting system was a rule-based model that couldn't adapt to renewable energy variability. I designed and deployed a Temporal Fusion Transformer-based forecasting pipeline that accounts for weather, seasonal patterns, industrial demand cycles, and inter-regional dependencies — all while remaining interpretable enough for grid operators to trust and act on its outputs.

18%
MAPE reduction
5
Regions covered
Real-time
72-hour horizon
Full case study
What clients say

From the people I've built things with

Emre is the rare engineer who can move from a research paper to a deployed system without losing rigour on either end. He doesn't just hand off models — he integrates with the team and keeps the whole process calm. Our robotics stack is genuinely better because of him.

LK
Dr. Lena Köhler
Head of Robotics, Helios Robotics

We brought Emre in to solve a specific perception problem. Within two weeks he'd diagnosed three issues we hadn't even noticed. His ability to hold the whole system in his head — from training data to edge deployment — is remarkable.

MS
Marco Steinberg
CTO, Pulse Autonomy

The forecasting model Emre built has held up through a Turkish winter, two major industrial shutdowns, and a solar generation spike that broke our old system twice. It just works, and the code is clean enough that our team can actually maintain it.

AY
Ahmet Yıldırım
Chief Data Officer, Vektor Energy
How to Work Together

Engagement models that fit real projects

I work with organisations in four ways depending on the size, urgency, and nature of the problem. Every engagement begins with a candid conversation about whether I'm the right fit.

ML Project Engagement

Scoped, fixed-duration projects focused on building and deploying a specific machine learning system — from problem framing through to production handoff.

  • 4–12 weeks per engagement
  • Full lifecycle ownership
  • Evaluation & handoff documentation
  • Post-launch support period included

Embedded Engineering

I join your team for an extended period as a senior engineer — attending standups, writing code alongside your engineers, and contributing to architecture decisions.

  • 3–12 month retainer
  • Embedded in your engineering team
  • Code ownership by your team
  • Mentorship of junior engineers

Research Collaboration

Joint research projects with academic institutions, research labs, or innovation-focused companies. Typically produces papers, open-source tools, or a funded prototype.

  • Co-authorship on publications
  • Grant writing support
  • Experimental design & analysis
  • Open-source deliverables

Advisory & Audit

A short, focused engagement to evaluate your current ML system or technical approach — useful before a major investment, a fundraise, or a production failure post-mortem.

  • 1–2 week turnaround
  • Written technical report
  • 90-minute strategy session
  • No obligation to continue

Interested in working together?

I have limited availability each quarter. If you have a project worth talking about, reach out and let's find out if I'm the right fit.

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