We are looking for a highly organized and experienced AI/ML Engineer to join our USV (Uncrewed Surface Vessel) team. Our innovative USV’s are widely celebrated within the maritime industry. Our products are used for subsea inspections, seafloor mapping and environmental monitoring. Using our USV’s reduces operational costs, human risks, and CO2 emissions
As AI/ML Vision Engineer, you are responsible for developing and supervising the model trainer for sensing (vision). But you also manage data models, and coordinate with consultants or third parties if needed.
This role involves setting up a comprehensive model trainer system using data from assets, user feedback, and updates to the model.
You work closely together with the Engineering Manager and Product manager to steer the strategy around AI and ML. In collaboration with the development team, you bring this strategy to life and integrate models into the software development lifecycle.
We invite those to apply with the creativity and right mindset to take on technical challenges and find new solutions.

Your role

• Setup Training Pipelines: Design and implement robust training pipelines in the cloud for computer vision applications.
• Automated Data Labelling: Develop and maintain (semi) automated data labelling systems to streamline the data preparation process.
• Model Testing and Benchmarking: Establish comprehensive testing and benchmarking protocols for machine learning models to ensure high performance and reliability.
• Field Testing: Conduct tests in the field with real hardware to validate model performance and gather data for continuous improvement.

Your trackrecord

• Educational Background: Bachelor’s or master’s degree in computer science, Machine Learning, or a related field. Preferred qualifications are in the maritime or autonomous systems industry and familiarity with hardware integration and real-world testing environments
• Experience: Proven experience in setting up cloud-based training pipelines, data labelling, and model testing.
• Technical Skills: Proficiency in Python, TensorFlow, PyTorch, and other relevant machine learning frameworks. Experience with model optimization frameworks, such as ONNX and TensorRT. Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• QA and Deployment: Experience with quality assurance processes and deploying machine learning models into production environments.
• Problem-Solving: Strong analytical and problem-solving skills with the ability to work as part of a team.
• Communication: Excellent verbal and written communication skills to effectively collaborate with cross-functional teams.

What Fugro offers

• A competitive salary;
• 29 holidays per year based on a fulltime employment (of which 4 are appointed by Fugro management) and the possibility to purchase 12 additional days;
• Extensive career & training opportunities both nationally and internationally;
• Flexible working hours and the ability to work from home in accordance with your manager and corporate policies;
• Commuting allowance;
• Modern pension scheme;
• Collective health insurance;
• Possibility to register with our corporate fitness plan;
• Coaching options through our EAP (Employee Assistance Program).

Are you interested?
Please visit our Company Page to find out more on what it is like to work at Fugro.
For further information, please contact Bram van der Ploeg, Talent Acquisition Partner, +31 6 3458 0051.

After you have applied

• You will receive an automated confirmation-email of the receipt of your application;
• When we see a match, we will invite you for the first interview within a couple of days. Of course, you will also receive a message if we will not invite you;
• after a successful first round, you will be invited for the second round;
• If we are both still positive after the second interview, we will make you an offer, and with that we hope to welcome you at Fugro!

Disclaimer for recruitment agencies:
Fugro does not accept any unsolicited applications from recruitment agencies. Acquisition to Fugro Recruitment or any Fugro employee is not appreciated.

• Setup Training Pipelines: Design and implement robust training pipelines in the cloud for computer vision applications.
• Automated Data Labelling: Develop and maintain (semi) automated data labelling systems to streamline the data preparation process.
• Model Testing and Benchmarking: Establish comprehensive testing and benchmarking protocols for machine learning models to ensure high performance and reliability.
• Field Testing: Conduct tests in the field with real hardware to validate model performance and gather data for continuous improvement