# Staff AI Engineer

> Meeru AI Inc · Pleasanton, United States (Hybrid) · Full-time · Posted 2026-10-12

**Workplace:** hybrid

## Description

**About Meeru AI**

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.

**The Role**

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.

**What makes this role different**

-   Grounding is a hard requirement no fabricated or unsupported output, ever. Faithfulness is a gate, not a tuning goal
-   Clear separation of concerns ML surfaces and prioritizes; generated text only states what is supported by verified data or confirmed by a human
-   Per-customer intelligence models that learn each business's behavior, with strict anti-leakage discipline
-   Deploys in customer clouds including managed LLMs in-VPC, so customer data never leaves their environment

## Requirements

-   **Staff-level applied ML/AI experience in production.** You have shipped and owned real ML and AI systems used by customers, and you have set technical direction for others, not only built prototypes.
-   **Strong classical machine learning.** Hands-on depth in time-series modeling, anomaly and outlier detection, and gradient-boosted models such as LightGBM or XGBoost, using Python, scikit-learn and pandas or Polars.
-   **Modern LLM and agent patterns.** You have built retrieval-augmented generation (RAG), tool-using agents, and structured (schema-validated) output, and orchestrated them with LangGraph or a similar framework.
-   **Rigor on evaluation, grounding and hallucination control.** You know how to measure whether an LLM's output is faithful to its source data, and you have built guardrails that stop unsupported statements from reaching users.
-   **Discipline around data leakage and honest evaluation.** You design features, splits and validation so models are never trained or scored on information they would not have in real use.
-   **Clear thinking about what ML can and cannot claim.** You can explain to non-ML colleagues where a model's output is evidence and where it is only a signal, and why that line matters when results sit next to reported financials.
-   **Real software engineering.** You write tested, production-grade Python services, are comfortable with SQL against warehouses like Snowflake or PostgreSQL, and have shipped containerized software.
-   **Technical leadership.** You mentor engineers and work closely with backend, data, product and evaluation colleagues to turn feedback into lasting improvements.

### Nice-to-haves

-   **Finance or accounting domain experience.** Familiarity with ERP, billing or FP&A data and the audit expectations around it.
-   **Feature-attribution literacy.** Experience with tools like SHAP, and a clear view of where attribution explanations break down.
-   **Human-in-the-loop systems.** You have designed review, confirmation or correction workflows and fed them back into models.
-   **Managed LLMs inside customer clouds.** Experience running AWS Bedrock, Google Vertex AI or Azure OpenAI inside a customer's VPC so their data never leaves their environment.
-   **Sparse and zero-inflated data.** Experience modeling rare events or data that is mostly zeros.
-   **Multi-deployment enterprise software.** Exposure to products shipped as multi-tenant SaaS, single-tenant SaaS and on-premises across AWS, Azure and GCP.

## Apply

[Apply at Meeru AI Inc](https://apply.workable.com/meeru-ai-inc/j/9D4360F352/apply)

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