Software Engineer, AI (Senior/Staff)

About the role

Monarch is a powerful, all-in-one personal finance platform designed to help make the complexity of finances feel simple again. Since launching in 2021, we've become the top-recommended personal finance app by users and experts. Our goal? To take the stress out of finances so our members can focus on what truly matters. We are a team of do-ers led by experienced entrepreneurs who are passionate about helping our members reach their financial goals. We're hyper focused on building a product people love, and on finding every edge that helps us do that better. AI is core to how we operate: every person on the team uses it as a partner to sharpen judgment, move faster, and expand what's possible. We're not looking for tool mastery, we're looking for fluency and curiosity. What matters is that AI is part of how you work today and that you're actively raising your own bar on how to use it well. As a fully remote company (even before COVID!), we welcome applicants from almost anywhere. Our team collaborates synchronously mostly from 9 AM - 2 PM PT and embraces asynchronous work to stay connected across time zones. Join us on our mission to transform lives by simplifying money, together. At Monarch, AI is the engine powering intelligent, personalized financial experiences for our users. We're looking for an AI Engineer to design, build, and own the features that help hundreds of thousands of users understand and manage their money. You'll work across the full spectrum of AI development, from prompt engineering and API integrations to building multi-agent systems and fine-tuning language models. You will be a force multiplier for Monarch's product, making critical decisions on everything from our conversational AI architecture to how we evaluate and ship AI features with confidence. You'll collaborate closely with our AI Platform team, who manage the underlying infrastructure, observability, and LLM routing. Your focus will be on the AI application layer: building intelligent features, advancing our ML systems, and ensuring quality through rigorous evaluation.

Responsibilities

• Choose the Right Tool for Each Problem: Navigate the AI toolkit thoughtfully. Know when a well-crafted prompt suffices, when retrieval systems add value, and when custom models are worth the investment. You'll balance innovation with pragmatism to ship features that work reliably at scale.
• Ship with Confidence: Leverage and enhance our sophisticated evaluation framework to ensure AI quality. You'll design test datasets, implement new scorers, and use our Braintrust-based eval system to validate changes before they reach users.
• A Partnership with AI Platform
• You Own: AI feature development, agent design and orchestration, ML model improvements, evaluation datasets and scorers, prompt engineering, and feature-level quality.
• AI Platform Owns: LLM routing and provider management, observability and cost attribution, infrastructure reliability, and shared AI services.
• Together You Own: End-to-end feature quality, evaluation frameworks, production incident response, and AI roadmap priorities.

Requirements

• 5+ years of experience in software engineering, with at least 2 years focused on building and operating production ML/AI systems.
• A proven track record of shipping LLM-powered features, with deep, hands-on expertise in prompt engineering, RAG systems, and evaluation techniques.
• Strong fundamentals in machine learning: embeddings, similarity search, classification, and probabilistic reasoning.
• Demonstrated experience building and using AI evaluation tooling (e.g., golden sets, rubric scoring, LLM-as-judge).
• Excellent Python skills and a history of building production-grade AI features and services.
• Strong collaboration and communication skills with a sharp product sensibility.
• A strategic mindset, comfortable making build-vs-buy decisions and designing features for long-term reliability.
• Multi-Agent Systems: Designing and building complex LLM orchestration with frameworks like LangGraph, CrewAI, or AutoGen.
• Fine-Tuning: Hands-on experience with LoRA, RLHF, or full fine-tuning on platforms like Vertex AI.
• Fintech Domain: Background in personal finance, banking, or data-rich consumer financial applications.
• Vector Databases: Hands-on experience with OpenSearch, pgvector, Pinecone, or similar at scale.
• Safety & Evaluation: Experience with red-teaming exercises, adversarial testing, and implementing guardrails.
• Typical Process:
• Recruiter Video Call
• Hiring Manager Video Call
• Technical Assessment (Live Coding)
• Virtual Onsite consisting of 3 rounds
• Reference Checks
• Offer

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