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Europe/LATAM
Remote only
We're building the front door for how businesses partner with government in America.
Public agencies spend roughly a trillion dollars a year with private companies, and nearly all of it still moves through infrastructure that hasn't changed in decades. Starbridge is building the full operating system for that relationship: the data, the signals, the workflows, and the rails that let a company find a public buyer, win the work, and get paid.
We monitor 320,000+ public sector entities around the clock. 500+ customers have joined us since our launch in March. We've raised $50M from top investors including Craft and Michael Seibel of Y Combinator.
We’re looking for an AI Engineer to help us build and scale the next generation of AI-powered systems at Starbridge. You’ll work on projects that sit at the core of our product—building, evaluating, and deploying LLM-driven features that make it easier for organizations to access, understand, and act on complex public data.
You’ll collaborate closely with our engineering and product teams to design new AI capabilities, such as deep document analysis and interactive chat experiences. This role combines hands-on software engineering, applied machine learning, and product experimentation, with the goal of delivering reliable, high-performing AI systems to production.
You’ll have the opportunity to work with models from OpenAI, Anthropic, and Gemini, contribute to our AI infrastructure, and play a key role in shaping how we leverage generative AI across our platform.
What you'll do:
Collaborate closely with our team as we productize new AI-powered capabilities: such as AI proposal writing & search experiences.
Evaluate and monitor the performance of AI models (we will work with models from OpenAI, Anthropic, Gemini, Parallel.ai) & systems through rigorous testing and experimentation.
Stay up-to-date with the latest advancements in AI and machine learning research, and proactively suggest improvements to enhance our generative AI capabilities.
Implement strong testing and CI/CD practices that help us move with confidence in our AI system development
What you bring:
Bachelor’s degree in Computer Science, Engineering, Mathematics, related field, or equivalent experience
Have experience working in a startup or a smaller company with real engineering impact.
5+ years of professional experience in software engineering, AI/ML development including:
Proficiency with production software (Python) and systems design
Machine learning algorithms and model development techniques
ML lifecycle tools like MLflow, dvc, weights & biases
Cloud deployment of ML systems
Professional experience with LLMs and large-scale models
Very strong software engineering skills with a track record of building scalable, distributed product machine learning systems
Product thinking: ability to take product requirements and strategize how to apply LLMs
Ability to communicate complex ideas and concepts effectively
Bonus if you have:
Experience building scalable applications with LLMs, using frameworks such as LangGraph, LiteLLM, Agent Client Protocol, and Koog
Depth of knowledge with RAG implementation and improvements
Proficiency with Kotlin
Interview process
We move fast — really fast. Getting back to someone today beats tomorrow, and our interview process reflects that mindset. Please keep us posted on your timeline so we can move quickly and speed things up where needed. All interviews are conducted via Google Meet.
How we work
Build Bridges to Help Customers Win - We pride ourselves on being obsessively customer-centric.
Shooting Star Speed - We move with extreme speed. We value momentum, decisiveness, and the ability to accelerate when it matters.
Fun Is a Feature - We intentionally design Starbridge to be an energizing place to do hard work. Joy, humor, and camaraderie make great teams faster, sharper, and more resilient.
In the Arena - We believe the best ideas come from living close to the work, feeling the friction, hearing the nuance, and experiencing the details firsthand.
Competitive salary + early-stage equity
Unlimited PTO
Regular offsites (NYC + global locations)