Capitol builds intelligence infrastructure for companies overwhelmed by data but starved for clarity. Today, 80% of enterprise AI projects fail because outputs aren’t accurate, attributable, or usable. Capitol AI works with companies in high-stakes industries to turn information into intelligence that drives decisions with model-agnostic, purpose-built infrastructure.

Capitol is building a different future - one where institutions, creators, and analysts stay in control of their knowledge, their IP, and their value. We deliver a model-agnostic platform that turns complexity into decision-grade insight. We don’t believe the future should be dictated by a handful of closed models. We enforce attribution, minimize hallucinations, and preserve temporal accuracy - giving companies trusted answers they can act on.

Our Vision

Agentic AI for every enterprise - model agnostic, embedded, governed, and decision-grade - built to power action and impact at scale.

Role Overview

We're seeking a Machine Learning Engineer to lead our LLM evaluation and new model adoption/integration process. This critical role will drive the continuous improvement of our AI capabilities, ensuring Capitol AI remains at the forefront of multimodal content creation. The ideal candidate will have expertise in….. and experience with large language models (LLMs), combined with proven delivery in tech startups.

Key Responsibilities

• Lead the development and implementation of comprehensive evaluation methodologies for our LLM systems
• Spearhead the process of identifying, evaluating, and integrating new language models into our platform
• Design and conduct experiments to assess model performance in multimodal content generation scenarios
• Collaborate with product and  engineering teams to translate evaluation insights into concrete platform improvements
• Develop benchmarks and metrics to quantify the quality and effectiveness of generated content across various modalities
• Optimize model performance for both our consumer-facing tool and API-integrated enterprise solutions
• Stay abreast of the latest developments in LLM technology and evaluation techniques

Required Qualifications

• MA or PhD in Computer Science, Machine Learning, or related field, with a focus on Neural Networks, NLP or multimodal AI systems
• 3+ years of experience in applied machine learning
• Extensive experience with Python and deep learning frameworks such as PyTorch or TensorFlow
• Proven track record in developing evaluation metrics and methodologies for complex AI systems
• Strong background in NLP, including experience with state-of-the-art language models
• Experience with LLM fine-tuning and prompt engineering

Preferred Qualifications

• Familiarity with multimodal content generation and document processing
• Familiarity with cloud platforms (AWS, GCP, or Azure) and MLOps tools
• Experience with API design and integration for AI services

What We Offer

• Opportunity to shape the future of AI-driven content creation
• Work with data from leading organizations
• Meaningful Equity participation
• Remote work arrangement

About Us

Capitol AI is agentic AI that partners with owners of large proprietary data sets (such as Politico Pro) to enable deeper insights from unstructured data and unlock new revenue opportunities with their clients in a highly efficient and impactful way.

Capitol has an in-house LLM orchestration layer with a generation pipeline that includes our own implementation of function calling, RAG, and chain of thought reasoning. We also have our own fine-tuning pipeline for function-specific small models. Our backend is python, our cloud is managed in terraform, our application CRUD is Clojure (LISP fans welcome) and our frontend is React.