Senior ML Engineer (AI)

TL;DR

Senior ML Engineer (AI): Developing accurate machine learning models of industrial equipment and system behavior from experimental and observational data with an accent on deep learning, data harmonization, and production-ready implementation. Focus on designing experiments, adapting complex architectures, integrating LLM-assisted development, and translating industrial requirements into reliable ML solutions.

Location: Fully remote within EU countries

Company

hirify.global provides business and technology advisory, enterprise solutions, design, managed services, product development, and software development.

What you will do

• Preprocess, clean, harmonize, and prepare real-world datasets for machine learning models.
• Select, adapt, and combine deep learning architectures for specific industrial use cases.
• Build supporting infrastructure and integrations around ML models.
• Use and supervise LLM-generated solutions to accelerate development where appropriate.
• Design experiments, evaluate model performance, and document results and recommendations.
• Collaborate with engineering and domain experts to translate industrial requirements into effective ML solutions.

Requirements

• Senior-level experience in machine learning, deep learning, and practical model development.
• Experience working with real-world datasets, including preprocessing and harmonization.
• Strong understanding of deep learning architectures and model adaptation.
• Experience designing and evaluating ML experiments.
• Strong Python and software engineering skills.
• Ability to work fully remotely from an EU country.

Nice to have

• Experience with A/B testing and experimentation.
• Experience with recommender systems or related ML domains.
• Experience in industrial software, industrial engineering, or industrial applications.

Culture & Benefits

• Fully remote work within EU countries.
• Equal opportunities in recruitment, career development, and leadership.
• Inclusive environment that values diverse backgrounds and perspectives.
• Collaboration with engineering, domain, and customer stakeholders.

Hiring process

• Application review followed by a Talent Acquisition interview.
• Technical interview focused on expertise, problem-solving, and engineering approach.
• Customer interview may be included, followed by a job offer for successful candidates.