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
About the Role
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.
What You’ll Do
Data Management & Architecture
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
Application Development
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
LLM Integration & Observability
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
Analysis & Insight
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
Collaboration & Ownership
Work closely with other engineers, product, and other stakeholders
Take end-to-end ownership of systems from design through production and iteration
What We’re Looking For
Required Skills & Experience
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
Nice to Have
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
What Success Looks Like
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
The Impact of this role
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.
Our stack
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
Benefits
Supportive and smart colleagues, flexible work, opportunity to make a big impact, competitive salary & equity, health, dental, vision, generous PTO, home office stipend.