Requirements

Technical Skills

• 4+ years of experience as a Software Engineer, with at least 3 years in an ML Engineer role
• Strong understanding of machine learning techniques, including supervised & unsupervised learning, NLP, deep learning fundamentals, and model evaluation
• Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-Learn, Pandas, and NumPy
• Hands-on experience in containerizing ML applications using Docker for scalable deployment
• Practical experience with at least one cloud provider (AWS, GCP)
• Strong background in working with large datasets, SQL/NoSQL databases
• Ability to decompose complex problems into well-structured ML tasks
• Skilled at assessing whether ML is the best approach or if a simpler solution (e.g., heuristic rules, statistical methods) would be more effective
• Expertise in debugging, optimizing, and enhancing models for performance, efficiency, and interpretability
• Experience maintaining ML workflows to ensure reproducibility, scalability, and operational efficiency

Business & Collaboration

• Excellent communication skills, capable of explaining ML concepts to both technical peers and non-technical stakeholders
• Collaborative, product-focused approach within Agile, cross-functional environments
• Proactive mindset with a strong sense of ownership with the ability to lead ML tasks end-to-end, from discovery and experimentation to production deployment and support
• Experience working closely with Product, Engineering, Data, DevOps, and business teams to align technical solutions with business goals
• Continuous learning mindset with awareness of current ML/AI trends, tools, and best practices
• English proficiency at an Upper-Intermediate level or above

Nice to have

• Understanding the business impact of ML models and how to align them with organizational goals
• Experience with feature stores, model registries, and ML model lifecycle management
• Experience designing and developing Retrieval-Augmented Generation (RAG) solutions
• Hands-on experience with AI tools in ML workflows

Benefits

Why people stay First, the foundation:

• Remote flexibility: Work where and how you work best - we trust you to deliver
• Fair compensation: Competitive salary + benefits that matter (medical, wellness, learning)

Then, the growth:

• Ownership opportunities: See a problem worth solving? Own it. We back smart risks over bureaucratic safety
• AI enhancement: We leverage AI to make you faster and stronger - complementing your abilities, not replacing them
• Learning investment: English classes, professional development, well-being support
• Career progression: Real paths up, not just sideways shuffling

Finally, the people:

• Responsive teammates: No ignored Slacks, no "not my problem" attitudes
• Supportive culture: When you're stuck, people help. When things break, we fix them together
• Human connections: Regular meetups, tech talks, and actual relationships beyond work