Data/ML Engineer

Location: Austria (Remote) | Team: Engineering

SluiceboxAI is building scalable, automation-driven sustainability intelligence for manufacturers. Our platform helps companies navigate compliance, reduce emissions, and drive transparency with automated Life Cycle Assessments (LCA). We’re a fast-moving startup where engineers play a central role in defining solutions, not just building features.

The Role

We’re looking for a strong ML/Data Engineer who can build and scale high-impact data and machine learning solutions. The ideal candidate is not just technical but has a product mindset, curiosity, and the ability to navigate ambiguity. This is an opportunity to own solutions end-to-end, work closely with product and customers, and help shape how we solve complex industry problems through data and AI.

What You’ll Do

• Build scalable data pipelines, ETL/ELT systems, and model deployment pipelines.
• Own the design, development, and optimization of ML workflows and databases.
• Work across the stack—primarily back-end and data engineering, with occasional front-end contributions when needed.
• Collaborate with product, engineering, and customers to deeply understand pain points and drive solution design.
• Implement automation, data validation, model evaluation, and continuous improvement systems.
• (Optional) Explore cutting-edge ML techniques for document parsing, carbon estimation, and system optimization.
  
  
  

Why Join Us?

• Be a builder. Work directly with leadership to define solutions, not just implement them.
• Own your work. Build impactful solutions that directly help customers and the planet.
• Move fast. Minimal red tape—you’ll see your impact quickly.
• Grow with us. Help shape our technical foundation and future as we scale.
  
  
  

If you thrive in startup environments, love solving hard problems, and want to help build something meaningful, we’d love to hear from you!

What We’re Looking For

Must-Haves:

• Strong Python experience, particularly in data engineering and/or ML workflows.
• Experience building and scaling ETL/ELT pipelines and working with relational databases (PostgreSQL preferred).
• Strong understanding of model training, deployment, and monitoring practices.
• Product thinking—ability to define solutions, not just execute tickets.
• Comfortable with ambiguity, startup pace, and fast problem-solving.
  
  
  

Nice-to-Haves:

• Experience with MLops, vector databases, embeddings, or related fields.
• Exposure to AI/ML model serving (e.g., using FastAPI, TorchServe, TensorFlow Serving).
• Previous leadership or ownership of high-impact data or ML projects.
• Experience with sustainability, LCA, or environmental data (big bonus, but not required).