Machine Learning Engineer (SaaS Product) to $3500 Offline

📍 Location: Fully Remote
🕒 Schedule: Flexible 8-hour day (preferably starting after 12:00 UA time)
💼 Type: Full-time, Long-term
💰 Payment: USD

About the Company

We’re a fast-growing SaaS company building AI-powered solutions for businesses across North America and Europe. Our mission is to unlock smarter decision-making through automation and intelligent data tools. We're scaling rapidly and are now looking for a Machine Learning Engineer to help us evolve our product capabilities.

What You’ll Be Doing

• Designing and implementing ML models that support intelligent features in our core SaaS platform
• Developing and maintaining data pipelines for model training and inference
• Creating AI-powered assistants and chatbots for internal and customer-facing solutions
• Optimizing ML models for scalability, speed, and performance in production environments
• Working with LLMs and external APIs to deliver smart automation features
• Analyzing product data to drive continuous improvement
• Collaborating with cross-functional teams (product, data, backend) in a lean agile environment
• Participating in short daily standups (max 30 min)

What We’re Looking For

• 2+ years of hands-on experience as an ML or Data Engineer
• Solid understanding of machine learning algorithms, model deployment, and evaluation
• Proficiency in Python (must-have), with working knowledge of SQL and Git
• Experience integrating ML models into production-ready SaaS applications
• Ability to work independently, take ownership, and ship results
• Enthusiastic about AI, automation, and shaping product features through data

Why Join Us?

• Work on meaningful, user-facing SaaS products with real-world impact
• Enjoy remote freedom with flexible hours
• Collaborate with a sharp, international team that values innovation and clean code
• Competitive compensation in USD
• Room for growth in a product-led company with strong tech foundations

Interested?
Shoot us a message — we’re happy to share more about the product, stack, and team.