Machine Learning Engineer (AI)

TL;DR

Machine Learning Engineer (AI): Building and optimizing AI and optimization features for a workforce management platform with an accent on advancing forecasting and optimization algorithms. Focus on translating new product features into mathematical constraints, implementing solutions in a Python codebase, and integrating into a microservice-based architecture.

Location: This is a hybrid role, requiring at least 2 days from the office. The specific office location is not mentioned.

Company

hirify.global is a leading workforce management platform that simplifies scheduling, optimizes staffing, and engages frontline teams globally.

What you will do

• Build core AI and optimisation features for a workforce management system.
• Advance forecasting and optimisation algorithms.
• Collaborate with the team on quality and timely project delivery.
• Assist in identifying key initiatives and contribute to their completion.
• Develop products focusing on automation, AI, reporting, and analytics.
• Participate in technical discussions within the broader R&D team.

Requirements

• 2-3 years of experience as an ML Engineer or similar role.
• Proficient in Python and/or JavaScript or related programming language.
• Ability to take full ownership of tasks and deliver high-quality outcomes.
• Strong communication skills for sharing ideas and presenting concepts.
• CVs must be written in English.

Nice to have

• Experience with Mathematical Optimisation Problems (MIP/LP).
• Experience with MLOps or DevOps in a Machine Learning context.
• Experience with cloud platforms (AWS).
• Experience with version control systems (Git).

Culture & Benefits

• Flexible work hours and a hybrid setup.
• Enhanced vacation allowance, gym membership contribution, health insurance, and pension plan.
• Truly international team.
• Opportunities for growth and impact.
• Open, inclusive, and fun environment.

Hiring process

• Short application.
• Psychometric and objective assessments via Alva Labs.
• Culture Interview with the Talent team.
• Meeting with the hiring manager.
• Role-specific technical assessment.
• Final in-office interview.
• References and background checks.