Base Salary
$250k - $350k/yr
Responsibilities
- Build distributed infrastructure that orchestrates and runs large numbers of agents in parallel with reliability, retries, and graceful degradation.
- Develop evaluation frameworks to measure agent quality, catch regressions, and validate performance across research tasks.
- Optimize agent performance through context compression, prompt optimization, model routing, and latency reduction.
- Build ontology and provenance systems that map concepts to definitions and trace outputs to authoritative sources.
- Integrate language models into production research workflows with error handling, fallback mechanisms, and cost optimization.
- Own services, database optimization, deployment, monitoring, testing, and production reliability end to end.
- Ship production systems with deployment pipelines, monitoring, and comprehensive testing.
- Take ownership of a core agentic system from architecture through production.
Requirements
- 7+ years of software engineering experience shipping production systems at scale.
- Backend experience with Python or Node.js, distributed systems, PostgreSQL, Redis, and AWS.
- Experience designing architectures that scale and handle complex workflows.
- Experience building with LLMs, agent frameworks, or ML infrastructure.
- Experience with large datasets, ETL pipelines, knowledge graphs, or semantic systems is a plus.
- Familiarity with Git workflows, automated testing, and observability.
- Strong communication skills for clearly discussing technical trade-offs.
- Curiosity about AI agents and willingness to work in a fast-paced, high-ownership environment.
- Financial services experience is preferred but not required.
Benefits
- Comprehensive medical, dental, vision, 401k, and insurance for employees and dependents.
- Automatic basic life, AD&D, and disability insurance coverage.
- Daily lunch in the office.
- Development environment budget including a MacBook Pro, monitors, ergonomic setup, and development tools.
- Unlimited PTO.
- Build-anything budget for tools, libraries, datasets, or infrastructure.
- Learning budget for conferences, courses, or programs.
- Direct mentorship, weekly 1:1s, architectural reviews, and a technical leadership growth path.
Tech Stack
Categories
About Kepler
We're building the ground truth platform for AI. AI is great at understanding what you're asking for. It's terrible at giving you answers you can trust. Everyone else is trying to make AI more accurate. We made accuracy the only possible outcome by building a platform that separates what AI does well from what code does well. AI handles the conversation. Code handles the truth. The result is the first AI system that can show its work. We automatically ingest data from scattered sources, structure it into a unified platform, and deploy specialized AI agents that conduct deep research with full transparency. Every insight links back to authoritative sources. Every conclusion reveals its reasoning. Every answer can be defended with complete confidence. We're starting in finance, where being wrong costs millions and speed wins deals, but we're building the foundational data layer for the AI era, applicable anywhere that decisions depend on trustworthy data. We're a team of ex-Palantir engineers who built data infrastructure for the world's most demanding organizations. We've raised from top investors, we're working with partners who need this yesterday, and we're building a small team obsessed with foundational technology. If you want to build the platform that becomes the ground truth for AI, we're just getting started.