Asks for

The ad says“Extensive hands-on experience designing, building, deploying and operating production ML systems, including data/ML pipelines, model evaluation, observability and productization in a cloud environment.”
The ad says“Extensive hands-on experience designing, building, deploying and operating production ML systems, including data/ML pipelines, model evaluation, observability and productization in a cloud environment.”
The ad says“A solid practical foundation in machine learning and software engineering, with at least 8 years of relevant professional machine learning experience, including substantial hands-on expertise building and operating ML products in production.”
The ad says“Superior knowledge of Python and the PyData ecosystem (pandas, scikit-learn, pytorch / tensorflow) as well as MLOps frameworks (MLFlow, Weights & Biases or similar)”
The ad says“Superior knowledge of Python and the PyData ecosystem (pandas, scikit-learn, pytorch / tensorflow) as well as MLOps frameworks (MLFlow, Weights & Biases or similar)”
The ad says“Excellent analytical skills and practical understanding of machine learning and statistics, with a focus on text-based (NLP) models.”
The ad says“Superior knowledge of Python and the PyData ecosystem (pandas, scikit-learn, pytorch / tensorflow) as well as MLOps frameworks (MLFlow, Weights & Biases or similar)”
The ad says“Superior knowledge of Python and the PyData ecosystem (pandas, scikit-learn, pytorch / tensorflow) as well as MLOps frameworks (MLFlow, Weights & Biases or similar)”
The ad says“Superior knowledge of Python and the PyData ecosystem (pandas, scikit-learn, pytorch / tensorflow) as well as MLOps frameworks (MLFlow, Weights & Biases or similar)”
The ad says“Superior knowledge of Python and the PyData ecosystem (pandas, scikit-learn, pytorch / tensorflow) as well as MLOps frameworks (MLFlow, Weights & Biases or similar)”
The ad says“A solid practical foundation in machine learning and software engineering, with at least 8 years of relevant professional machine learning experience, including substantial hands-on expertise building and operating ML products in production.”
The ad says“Excellent analytical skills and practical understanding of machine learning and statistics, with a focus on text-based (NLP) models.”
The ad says“Superior knowledge of Python and the PyData ecosystem (pandas, scikit-learn, pytorch / tensorflow) as well as MLOps frameworks (MLFlow, Weights & Biases or similar)”
The ad says“Superior knowledge of Python and the PyData ecosystem (pandas, scikit-learn, pytorch / tensorflow) as well as MLOps frameworks (MLFlow, Weights & Biases or similar)”

About Comply365

This is an excellent opportunity to join Comply365, the market leader in operational content management, safety management and training management serving the global aviation, defense and rail industries with a stellar customer base of 550+ customers in 80+ countries worldwide and delivering an industry first, connected platform, powered by AI, across these mission critical domains.
The Comply365 platform is an industry-first interconnected, intelligent platform with best-in-class solutions across Operational Content Management, Safety, Risk & Quality Management, and Training Management. Powered by industry-specific AI, our unified platform connects data and insights across operations, safety, and training - eliminating complexity and driving measurable gains in efficiency and productivity for aviation, rail and defense organizations worldwide.

About the role

SafetyManager365 teams work rapidly, adapt with agility, and stay focused on delivering meaningful solutions. Airlines generate huge volumes of safety reports, operational data and investigations. Our platform helps safety teams analyse that information, identify risks earlier and make better operational decisions. We are driven by the opportunity to solve complex challenges with real impact for our customers and the wider industry.

We are looking for an ML engineer to join our newly established team inside the larger Comply365 group. The ideal candidates would have hands-on experience deploying machine learning models in production and will thrive in a fast-paced, startup-like environment. We are a highly collaborative engineering culture and we write tests first, pair every day and commit directly to trunk.

Due to time-zone alignment and business needs, applicants must be able to work on a hybrid basis in Barcelona.

Key Responsibilities

Own ML initiatives end to end — from understanding customer problems and designing experiments through to implementation, deployment, measurement and continuous improvement.
Apply a strong product mindset when evaluating ML and AI technologies, choosing pragmatic solutions that deliver customer and business value over technical novelty.
Partner closely with product managers and engineers to design and deliver robust, production-ready ML and AI solutions.
Write clean, efficient and maintainable code, applying strong software engineering practices and maintaining high standards of quality.
Work collaboratively through regular pair programming, sharing knowledge, supporting others and continuously improving engineering practices.

Skills and Qualifications

A solid practical foundation in machine learning and software engineering, with 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 is welcome, but equivalent industry experience is also valued.
A pragmatic, iterative approach to ML development - comfortable running small, time-boxed experiments, learning quickly 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) as well as 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 a cloud environment.
Provide technical leadership for machine learning initiatives, taking ownership from problem definition and experimentation through to production delivery.
Coach and mentor other engineers, helping to raise ML capability across the team through pairing, knowledge sharing and technical guidance.
Excellent communication, self-awareness and interpersonal skills. Able to challenge technical ideas constructively, navigate disagreement with confidence and empathy, and build trust across a collaborative engineering team.
Strong software engineering fundamentals and experience designing and evolving production systems, with the ability to make sound architecture and engineering trade-offs.
A passion for the aviation industry and a desire to leverage AI technology to drive improvements and innovations in airline safety

Benefits

⌨️High-end equipment to facilitate your best work

💰Competitive salary and benefits package

🗣️Annual team offsite and access to our offices in Europe and a co-working space in your location

🏢A hybrid-remote work policy

Salary: €110,000 - €130,000 gross per annum. The salary offered will be determined within this range based on relevant experience, qualifications and demonstrated competencies.