Staff AI Engineer

Meeru AI Inc • Pleasanton, CA

18h

About The Position

Meeru AI is building an AI-native platform that transforms how finance and accounting teams operate. We connect to enterprise financial systems — ERPs, CRMs, billing platforms, HRIS — and apply machine learning to turn fragmented operational data into grounded, auditable intelligence for CFOs, controllers, and FP&A leaders.
We deploy on customer terms — SaaS multi-tenant, SaaS single-tenant, and on-premises — across AWS, Azure, and GCP. Our customers are Fortune 500 finance teams who require data isolation, auditability, and compliance.
We are looking for a Staff AI Engineer to own the AI and intelligence layer — and the discipline that keeps it honest. Our output sits adjacent to externally reported financials, so "sounds plausible" is not good enough: everything the AI produces must be grounded in, and traceable to, verified data.
You own the machine-learning models that learn each customer's patterns, the LLM and agentic systems that produce grounded natural-language output, and the rigor that keeps that output faithful. You build per-customer models without leaking the very signal they're meant to detect, and you set a high bar for evaluation and grounding across the layer.
This is a senior, hands-on role. You set technical direction for the AI layer, partner closely with the evaluation engineer to make quality measurable, and turn user feedback into durable improvements.

Requirements

• Staff-level applied ML/AI experience in production, with a track record of shipping and owning real ML and AI systems used by customers.
• Experience setting technical direction for AI systems, not just building prototypes.
• Strong classical machine learning skills, with hands-on depth in time-series modeling, anomaly and outlier detection, and gradient-boosted models (e.g., LightGBM, XGBoost).
• Proficiency in Python, scikit-learn, and pandas or Polars.
• Experience with modern LLM and agent patterns, including retrieval-augmented generation (RAG), tool-using agents, and structured (schema-validated) output.
• Experience orchestrating LLM systems with frameworks like LangGraph.
• Expertise in evaluation, grounding, and hallucination control for LLMs, including measuring output faithfulness to source data and building guardrails.
• Discipline around data leakage and honest evaluation, including designing features, splits, and validation to prevent training or scoring on future information.
• Ability to clearly articulate what ML can and cannot claim, explaining the difference between evidence and signal to non-ML colleagues.
• Real software engineering skills, including writing tested, production-grade Python services.
• Comfort with SQL against data warehouses like Snowflake or PostgreSQL.
• Experience shipping containerized software.

Nice To Haves

• Finance or accounting domain experience, including familiarity with ERP, billing, or FP&A data and audit expectations.
• Feature-attribution literacy, with experience in tools like SHAP and an understanding of attribution explanation limitations.
• Experience designing human-in-the-loop systems, including review, confirmation, or correction workflows that feed back into models.
• Experience running managed LLMs inside customer clouds (e.g., AWS Bedrock, Google Vertex AI, Azure OpenAI) within a customer's VPC.
• Experience modeling sparse and zero-inflated data, including rare events or data that is mostly zeros.
• Exposure to multi-deployment enterprise software shipped as multi-tenant SaaS, single-tenant SaaS, and on-premises across AWS, Azure, and GCP.

Responsibilities

• Own the AI and intelligence layer, ensuring all AI output is grounded in and traceable to verified data.
• Develop and maintain machine-learning models that learn each customer's patterns.
• Build LLM and agentic systems to produce grounded natural-language output.
• Implement rigorous evaluation and grounding techniques to ensure output faithfulness.
• Build per-customer models with strict anti-leakage discipline.
• Set technical direction for the AI layer.
• Partner with the evaluation engineer to make quality measurable.
• Translate user feedback into durable improvements for the AI layer.
• Mentor engineers and collaborate with backend, data, product, and evaluation teams.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed