Data & LLM Systems Engineer

Help define the future of hardware development! At AllSpice, we’re building the automation engine that powers the next generation of circuit design, enabling breakthroughs in smart vehicles, robotics, rockets, IoT, medical devices, and more.

We’re creating the agile development environment for hardware engineers with a Git-friendly translation layer and automated CI/CD framework for native circuit designs. Think GitHub/GitLab + Copilot for electronics.

Our mission is to bring the same modern workflows that revolutionized software: version control, automation, and collaboration to the hardware world.

Learn more about our journey in TechCrunch and our recent Series A announcement.

This role is a hands-on leadership position that will evolve as the GenAI team scales.

If you’re passionate about building automation systems and want to shape the foundation of modern hardware DevOps, we’d love to meet you!

Read more at https://allspice.io

We’re looking for a Data & LLM Systems Engineer to help us design, build, and operate the systems that sit at the intersection of hardware design, data, applications, and large language models (LLMs).

In this role, you’ll own how data flows from raw inputs into structured systems, how that data is exposed through our suite of applications, and how LLM interactions are instrumented, analyzed, and improved over time. You’ll work closely with our GenAI, Platform, and Infrastructure teams to ensure DRCY is reliable, observable, and continuously getting better.

This is not a research-only role and not a frontend-only role. It’s a hands-on engineering position focused on building real systems that people and products depend on.

• Design, build, and maintain data pipelines for ingesting, cleaning, transforming, and storing data

• Define and evolve data schemas that support analytics, applications, and LLM workflows

• Work with relational databases, analytical data stores, and vector databases

• Ensure data reliability, performance, and cost efficiency

• Implement best practices around data versioning, lineage, and access control

• Build backend services and APIs that expose data to internal tools and user-facing applications

• Develop applications and internal tooling for managing datasets, experiments, prompts, and configurations

• Collaborate with product and design to ensure tools are usable, safe, and scalable

• Support both real-time and batch processing workflows

• Design systems that track prompt versions, context construction, and model configurations

• Instrument LLM interactions to capture inputs, outputs, metadata, latency, and cost

• Help establish standards for monitoring, debugging, and evaluating LLM behavior in production

• Analyze LLM outputs and user interactions to identify failure modes, drift, and quality issues and help ensure overall reliability and consistency

• Define and track metrics related to response quality, task success, and user outcomes

• Run comparisons between model versions, prompt changes, or system configurations

• Communicate findings clearly to engineering, product, and leadership stakeholders using

• Work closely with other engineers, product, and other stakeholders

• Take end-to-end ownership of systems from design through production and iteration

• Strong experience building backend systems and APIs

• Solid understanding of data modeling, databases, and data pipelines

• Experience analyzing applications that leverage LLMs (e.g., OpenAI, Anthropic, open-source models) in production systems

• Comfort analyzing data and drawing actionable conclusions

• Ability to reason about system performance, reliability, and cost

• Experience with vector databases and semantic search

• Familiarity with analytics warehouses (e.g., BigQuery, Snowflake, Redshift)

• Experience with LLM observability, evaluation frameworks, or experimentation platforms

• Background working on internal developer tools or data platforms

• Exposure to privacy, security, or governance considerations in data systems

• Data is reliable, well-structured, and easy to use across the organization

• LLM-powered features are observable, debuggable, and improving over time

• Teams can safely interact with complex data and AI systems through well-designed tools that ensure data privacy

This role is central to turning raw data from our tools into practical, measurable outcomes. You’ll help shape how we build, understand, and trust AI-powered systems — not as experiments, but as core parts of the product.

• We primarily build in Python and this team will create LLM orchestration and agent frameworks using tools like Pydantic AI and Langchain

• Our Hub application is a soft fork of Gitea

  • Go [server-side]

  • Vue/TypeScript Front-end

• We leverage GitHub actions for CI/CD and to trigger our agents

• Docker Swarm & Terraform for deployment

• AWS

• Postgres DB

• Supportive and smart colleagues, flexible work, opportunity to make a big impact, competitive salary & equity, health, dental, vision, generous PTO, home office stipend.