Field Materials develops AI-native construction procurement software. Our AI agents automate workflows in procurement, AP, and inventory management. As a result, our customers spend less time on data entry and manual workflows in legacy ERPs. Our edge is in best-in-class AI and powerful integrations with existing construction ERPs. We're post-PMF and scaling: already processing $3B+ in material purchase volume across 16 construction trades. Tech Stack: - Generative AI + optimization algorithms drive document extraction. Models are heavily tuned for the domain. Algorithms, LLMs, and RAGs drive automations. Strong metric-oriented culture with evaluation sets across the board. - Go, GraphQL & Postgres (gqlgen, pgx, squirrel, dataloaders). Sophisticated data models aligned with ERPs. Complex authorization and approvals. In-house observability for GraphQL. AWS stack: ALB, ECS, RDS, S3. - Bidirectional integrations with unified data model across 13+ construction systems & on-prem connector. REST APIs, ODBC, COM, and others. - We spin out separate services where it makes sense. The construction data model is complex with a lot of business data constraints so we use ACID transactions whenever possible. We’re looking for AI engineers who want extreme ownership. You’re not just a research scientist; you own models, data pipelines, evaluation suites, and AI product features. About You: - You are excited about startups and new products. - You enjoy solving complex problems and iterating fast. - You realize that products are built iteratively and specs evolve with customer feedback. - You care about system and code design: well architected code is easier to understand, refactor, evolve, and inherit. - You enjoy optimizing AI-enabled features to automate tedious work with high financial impact for customers. - You prefer well thought-out, scientific solutions. What We Look For: - BS (or higher) in Mathematics, Computer Science, or related technical field. - 2-5+ years of production level experience in Backend Engineering or AI Engineering. - Experience in designing, evaluating and integrating AI models and data pipelines. - Experience with document data extraction problems. - Experience working with SQL databases and complex data models. - Strong foundation in algorithms and data structures and their real-world use cases. - Proficiency with Golang is a nice-to-have. Benefits: - Flexible time off - Equity awards - Gym reimbursement - Personal development fund for books, courses, certifications and conferences - Swanky new MacBook Pro - Work headphones reimbursement - Annual offsites at fun locations across Europe