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Salary: EUR 110,000 - 130,000 / annual
Comply365 is the market leader in operational content management, safety management, and training management for the global aviation, defense, and rail industries, serving 550+ customers across 80+ countries. The company has built an industry-first interconnected platform powered by AI that connects data and insights across operations, safety, and training.
You will join a newly established machine learning team within the larger Comply365 group, working on SafetyManager365—a product that helps airline safety teams analyze massive volumes of safety reports, operational data, and investigations to identify risks earlier and make better operational decisions.
In this role, you will own ML initiatives end-to-end: from understanding customer problems and designing experiments through implementation, deployment, measurement, and continuous improvement. You'll apply a strong product mindset when evaluating ML and AI technologies, choosing pragmatic solutions that deliver customer and business value over technical novelty. You'll partner closely with product managers and engineers to design and deliver robust, production-ready ML and AI solutions. The team operates with a startup-like velocity, writes tests first, pairs daily, and commits directly to trunk.
Key responsibilities include:
- Owning ML initiatives end-to-end, from problem understanding through deployment and continuous improvement
- Applying product thinking to ML/AI technology choices, prioritizing pragmatism and customer value
- Partnering with product and engineering teams to design and deliver production-ready solutions
- Writing clean, efficient, maintainable code with strong software engineering practices
- Collaborating through regular pair programming and knowledge sharing
- Providing technical leadership and mentoring other engineers to raise ML capability across the team
Due to time-zone alignment and business needs, applicants must be able to work on a hybrid basis in Barcelona.
REQUIREMENTS:
- At least 8 years of relevant professional machine learning experience, including substantial hands-on expertise building and operating ML products in production
- Scientific education in computer science, machine learning, or a related discipline (or equivalent industry experience)
- Pragmatic, iterative approach to ML development; comfortable running small, time-boxed experiments and changing direction based on evidence
- Excellent analytical skills and practical understanding of machine learning and statistics, with a focus on text-based (NLP) models
- Superior knowledge of Python and the PyData ecosystem (pandas, scikit-learn, PyTorch/TensorFlow)
- Experience with MLOps frameworks (MLFlow, Weights & Biases, or similar)
- Extensive hands-on experience designing, building, deploying, and operating production ML systems, including data/ML pipelines, model evaluation, observability, and productization in cloud environments
- Technical leadership capability for ML initiatives, taking ownership from problem definition through production delivery
- Ability to coach and mentor other engineers, raising ML capability through pairing and knowledge sharing
- Excellent communication, self-awareness, and interpersonal skills; ability to challenge ideas constructively and navigate disagreement with confidence and empathy
- Strong software engineering fundamentals and experience designing and evolving production systems
- Passion for the aviation industry and desire to leverage AI to drive improvements in airline safety
