- Clear ownership, not decision by consensus
- First principles over inherited patterns
- Shipping systems, not slide decks
- Fast feedback from reality, not opinions
- Own end-to-end systems architecture across data pipelines, AI/ML platforms, semantic layers, and application interfaces — designing for modularity, scale, and durability from day one.
- Build and evolve the data integration layer: ingestion, normalization, and orchestration across structured and unstructured sources, using API-first design principles (REST, GraphQL, gRPC) and real-time streaming technologies like Kafka and Apache Pulsar.
- Architect the semantic intelligence layer: knowledge graphs, ontology design, vector embeddings, and RAG techniques that give Auger context-aware reasoning across the full enterprise data fabric.
- Design and operate scalable AI/ML platforms for training, deployment, and model lifecycle management — integrating LLMs, embeddings, and multimodal models into production applications via MLOps tooling (MLflow, SageMaker, Databricks).
- Drive AI into the application layer: partner with product and design to ship agentic, adaptive user experiences that surface intelligence at the moment operators need it.
- Set architectural direction across the platform: make layer boundaries, evolution strategies, and tradeoffs explicit — and document decisions the team can execute against with confidence.
- Raise the bar on operational rigor: fault tolerance, high availability, observability, and performance at enterprise scale are non-negotiable properties, not afterthoughts.
- Mentor engineers on system design, coding standards, and operational excellence — and hold a high bar on what ships.
- Bachelor or Master's degree in Computer Science, Engineering, or a related field.
- 10+ years of experience in systems architecture, software engineering, and platform development — with a proven track record building scalable data platforms or AI-driven systems at enterprise scale.
- Deep programming expertise in Python, Java, or C++, and hands-on experience building distributed systems in cloud-native environments (Azure, AWS, or GCP, including multi-cloud).
- Fluency in real-time data processing and analytics frameworks (Spark, Kafka, Flink) and big data technologies (Databricks, Snowflake, Hadoop).
- Advanced understanding of semantic modeling, knowledge graphs, and ontology design — including graph databases, graph embeddings, link prediction, and GNNs.
- Hands-on experience with AI/ML pipeline design and deployment, including frameworks such as TensorFlow, PyTorch, or equivalent, and familiarity with architectural patterns including microservices, event-driven architectures, and domain-driven design.
- Technical leadership through ambiguity: you set direction, communicate tradeoffs clearly to technical and non-technical partners, and write crisp architecture decisions when the stakes are high.
Skills Required
- Bachelor or Master's degree in Computer Science, Engineering, or related field
- 10+ years of experience in systems architecture, software engineering, and platform development
- Deep programming expertise in Python, Java, or C++
- Hands-on experience building distributed systems in cloud-native environments (Azure, AWS, or GCP, including multi-cloud)
- Fluency in real-time data processing and analytics frameworks (Spark, Kafka, Flink) and big data technologies (Databricks, Snowflake, Hadoop)
- Advanced understanding of semantic modeling, knowledge graphs, ontology design, graph databases, embeddings, and GNNs
- Hands-on experience with AI/ML pipeline design and deployment, including TensorFlow, PyTorch, and MLOps tooling
- Experience integrating LLMs, embeddings, and multimodal models into production applications
- Experience with API-first design (REST, GraphQL, gRPC) and real-time streaming technologies (Kafka, Apache Pulsar)
- Familiarity with architectural patterns: microservices, event-driven architectures, and domain-driven design
- Proven technical leadership: set direction, communicate tradeoffs, and document architecture decisions
What We Do
About Auger Auger is a pioneering venture to build the world's first true end-to-end supply chain operating system. Founded and led by Dave Clark, former CEO of the Amazon Consumer Business and backed by an initial $100M from Oak HC/FT, Auger is building a future where global supply chains operate with the simplicity of today’s most intuitive consumer technologies. Revolutionizing global supply chains with an AI-powered OS unifying data for seamless, real-time insights, and powerful automation. Our Solution Auger is creating a new solution for companies seeking better options. Auger’s core strength lies in its deep AI-powered automation, paired with a consumer-grade user experience. This combination allows operators to handle complex tasks through simple, familiar tools. Need real-time inventory insights for next week’s shipment? Just ask. Actionable data appears instantly, enabling swift decisions—no complex queries or training required. Why We’re Different Traditional supply chain management is fragmented, relying on incompatible systems and inefficient workarounds. Many companies are stuck with “Franken-software”—patched-together solutions that fail to communicate effectively. Auger is different. We integrate deeply with existing systems, use AI to automate routine processes, and deliver a cohesive user experience that feels intuitive and natural, letting your team focus on what matters: driving growth, innovation, and sustainability. A Human-Centered Approach Broken supply chains don’t just impact businesses—they affect people. Delays mean products don’t reach shelves, miscommunications lead to overtime and burnout, and inefficiencies drive up costs and contribute to a growing carbon footprint. We believe supply chain problems are human problems, and we’re here to solve them. At Auger, we’re on a mission to make global supply chains more efficient, more sustainable, and ultimately, better for everyone.
