Machine Learning Research Engineer – Visual Navigation
Lausanne · Zurich · Paris · Lausanne, Canton de Vaud, Switzerland
- Pay
- Pay not listed
- Work setting
On-site — work setting source
Location type: On-site
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
R&D › G3 — team source
Department: R&D › G3
From the employer’s posting
What you’ll work on
Full postingDesign deep learning models that analyze a drone's environment and enable its localization
Embed models on limited-compute drone hardware, balancing speed, accuracy and latency
Build and manage training, evaluation and MLOps pipelines with version control
Take part in real-world flight tests and analyze in-field failures to improve models
Partner with VIO, Navigation and Embedded Software teams on a real-time localization pipeline
From the employer’s posting
Design deep learning models to analyze a drone's environment in multiple modalities and enable its localization through time and space.
Embed models on heterogeneous platforms with limited shared compute. Optimize trade-offs between model speed and accuracy to minimize pipeline latency.
Build, heavily modify, and manage training, evaluation, and MLOps pipelines while ensuring reproducibility, robust logging, and version control.
Actively participate in real-world flight tests. Analyze in-field failures to continuously iterate and improve deployed models.
Partner with the VIO, Navigation, and Embedded Software teams to build a robust, real-time localization pipeline
Tools in this posting
- Python
- PyTorch
Source — Tool mentions in context
- Deep Learning & Computer Vision Expertise: Proven track record of researching and productizing Deep Learning models for Computer Vision in the industry (Drones, Robotics, Autonomous Vehicles, etc.) - Technical Competence: Proficiency in Python and DL frameworks (PyTorch/Lightning). Experience with MLOps and edge deployment optimization. Professional Attributes:
About Harmattan AI
Harmattan AI is a next-generation defense prime building autonomous and scalable defense systems.
In the employer’s words · Read in context · Company website ↗
Posting record
Report an errorActively hiringFirst seen by us 12 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Oct 19, 2026 · 12 days ago
- Recorded sightings
- 19
- Last seen by us
- Nov 1, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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Job description
About Us
Harmattan AI is a next-generation defense prime building autonomous and scalable defense systems. Following the close of a $200M Series B, valuing the company at $1.4 billion, we are expanding our teams and capabilities to deliver mission-critical systems to allied forces.
Our work is guided by clear values: building technologies with real-world impact, pursuing excellence in everything we do, setting ambitious goals, and taking on the hardest technical challenges. We operate in a demanding environment where rigor, ownership, and execution are expected.
As an ML Research Engineer in the Visual Navigation team, you will join us at an early stage to heavily influence our technical direction. Operating out of Paris, Lausanne or Zurich, you will focus on developing Machine Learning models to enable our drones to navigate on the battlefield, with responsibilities ranging from data curation and model architecture definition to onboard inference on our drones.
Responsibilities
DL Algorithm Development: Design deep learning models to analyze a drone's environment in multiple modalities and enable its localization through time and space.
Edge AI Optimization: Embed models on heterogeneous platforms with limited shared compute. Optimize trade-offs between model speed and accuracy to minimize pipeline latency.
Cross-Team Collaboration: Partner with the VIO, Navigation, and Embedded Software teams to build a robust, real-time localization pipeline, communicating trade-offs clearly to stakeholders.
Research & Innovation: Stay at the absolute forefront of scientific trends in Deep Learning to introduce cutting-edge methodologies to the team.
Pipeline Management & MLOps: Build, heavily modify, and manage training, evaluation, and MLOps pipelines while ensuring reproducibility, robust logging, and version control.
Validation & Field Testing: Actively participate in real-world flight tests. Analyze in-field failures to continuously iterate and improve deployed models.
Candidate Requirements
Educational Background: Strong academic record with a STEM degree (e.g., Computer Science, Machine Learning, Robotics, Mathematics).
Deep Learning & Computer Vision Expertise: Proven track record of researching and productizing Deep Learning models for Computer Vision in the industry (Drones, Robotics, Autonomous Vehicles, etc.)
Technical Competence: Proficiency in Python and DL frameworks (PyTorch/Lightning). Experience with MLOps and edge deployment optimization.
Professional Attributes:
Highly structured, analytical, and research-oriented.
Excellent communicator able to translate complex algorithmic benchmarking to downstream users and stakeholders.
Thrives in a fast-paced environment with a hands-on, “no-task-is-too-small” mentality.
Commitment: 100% dedication to Harmattan AI’s mission, vision, and growth plans, ready to go the extra mile for operational excellence.
We look forward to hearing how you can help shape the future of autonomous defense systems at Harmattan AI.
Source details
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
Lausanne, Canton de Vaud, Switzerland
Working pattern and location restrictions need checking in the full posting.
- Work authorization
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