Process/Data Science Engineer

Location: Austria (Remote) | Team: Engineering

SluiceboxAI is transforming how electronics manufacturers understand and reduce their carbon footprint. We automate lifecycle assessments (LCAs) and emissions analysis at the component level—unlocking transparency and compliance in a $2T+ industry. Our platform combines carbon accounting, deep domain expertise, and data science to deliver accurate, scalable intelligence to OEMs, contract manufacturers, and suppliers.

We're a fast-moving startup where engineers define the "how," not just execute the "what."

The Role

We're hiring a true hybrid: a process-savvy Data Science/Engineer with deep knowledge of electronics manufacturing—someone who understands how components are actually made and can model, analyze, and structure that data into scalable emissions intelligence.

You'll help us build and refine the data pipelines, process models, and emissions logic that power our carbon intelligence engine. This means working at the intersection of semiconductor, substrate, and electronics process knowledge and data engineering, LCA, and uncertainty modeling. If you've ever wanted to combine shop-floor logic with advanced analytics for climate impact—this is that role.

What You'll Do

• Architect and scale data pipelines and APIs that power part-level emissions estimates across billions of components

• Translate real-world manufacturing steps—etching, deposition, reflow, dicing, testing—into structured data and emission models

• Write algorithms that reflect technical standards like ISO 14067, IPC-1783, and the GHG Protocol

• Integrate messy, multi-source data (from suppliers, factories, and BOMs) and build tools that clean, structure, and map it to process-level emissions

• Collaborate with sustainability experts to define functional units and allocation logic that reflect real component behavior

• Build frameworks for emissions traceability, sensitivity analysis, and uncertainty quantification

• Validate output against known benchmarks and supplier-provided documentation

Why Join Us

• Tackle climate change from the inside out. You'll help decarbonize the electronics supply chain by embedding emissions awareness where it matters—at the part level.

• Work at the intersection. This is a rare chance to bridge engineering reality and data modeling in a meaningful, measurable way.

• Build systems from scratch. You'll influence foundational models, standards integrations, and data architectures.

• Join a rocket ship. We're early-stage, well-funded, and already working with some of the biggest names in electronics.

If you've ever wanted to bring clarity to complex data, design carbon-aware systems, and have a real-world climate impact—we'd love to hear from you

What We're Looking For

Must-Haves

• Hands-on process engineering experience in electronics manufacturing: semiconductors, substrates, passive components, PCBs, or advanced packaging

• Strong Python and data engineering skills (ETL, SQL, APIs, data modeling)

• Proven ability to read and structure technical manufacturing data: layer stacks, process flows, BOMs, facility inputs, etc.

• Exposure to CO₂e modeling or strong motivation to contribute to climate-focused work

• Systems thinker with a builder's mindset—comfortable owning end-to-end technical challenges

• Fluency in concepts like mass-energy balance, yield, and emissions allocation

• Candidate has to be located in Austria with full-time working rights

Nice-to-Haves

• Knowledge of LCA standards and tools (ISO 14064, ISO 14067, EN 50693, OpenLCA, Ecoinvent, etc.)

• Experience working with factory IoT systems, DOE, or SPC tools

• Familiarity with chemistry-based processes (e.g., wet etch, plating, metallization)

• Experience with uncertainty modeling, Bayesian estimation, or vector embeddings

• Exposure to ML workflows or interest in applying AI to structured physical data

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