Job description

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.

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: