Senior Staff Backend Engineer Forward Deployment Engineering

Meeru AI Inc; Pleasanton (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Meeru AI Inc in Pleasanton, CA is seeking a Senior Staff Backend Engineer to own the core services behind the data-intensive platform. You will design reusable components, enforce guardrails, and ensure every result is reproducible and auditable.

You will lead backend engineering, partner with data, AI, and product teams, and shape architecture for reliability, isolation, and scalability in a multi-tenant SaaS environment.

Qualifications

• 8+ years of backend engineering in Python, Go, Java, or similar.
• Production Python experience with strong service design and testing.
• Elite SQL and data modeling for large-scale joins.
• Set-based, reproducible computation over a data warehouse (Snowflake or PostgreSQL).
• Configuration-driven architecture with extensible services.
• Strong system design for reliability and isolation.
• Correctness mindset: every result must be traceable, explainable, and auditable.
• Technical leadership with pattern setting and mentorship.

Skills

Backend engineering

Python

Java/Go

SQL modeling

Config-driven

System design

Tech leadership

Reproducible computation

Job 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 Senior Staff Backend Engineer to own the core services at the heart of the platform — the data-intensive computation layer that turns curated financial and operational data into correct, reproducible, fully traceable results. This is the structured engineering beneath the product: not dashboards, not an LLM wrapper, but the services that do the heavy computation and stand behind every number the platform produces.

You build reusable, configuration-driven computation components, enforce strong controls and guardrails over automated output, and guarantee that every result reconciles and can be reproduced and audited. You provide technical leadership for the backend team and partner with data, AI, and product to translate domain logic into robust, reusable services.

What makes this role different

The output of this layer sits next to externally reported financials, so correctness is not negotiable. Everything you build is held to a hard standard: every result must be traceable to source, reproducible, and auditable, with clear guardrails that keep verifiable results separate from anything that requires human review — and nothing is ever quietly fudged to make the math look complete.

• 8+ years of backend engineering in Python, Go, Java, or similar, including significant time at Staff+ level owning complex, data-intensive systems.
• Production Python with strong service design and testing practices.
• Elite SQL and data modeling. You’re fluent in window functions and large-scale joins, and you reason rigorously about both correctness and performance.
• Set-based, reproducible computation over a data warehouse such as Snowflake or PostgreSQL.
• Configuration-driven architecture. You’ve designed extensible services or rules engines where new use cases ship as configuration, not new code paths.
• Strong system design for reliability, isolation, and reproducibility.
• A correctness mindset. You treat “every result is traceable, explainable, and reproducible” as a hard requirement.
• Technical leadership. You set patterns, review designs, and mentor engineers.

Nice-to-Haves

• Finance, accounting, or analytics domain experience, or genuine curiosity about how financial statements are built and reconciled.
• dbt alongside Snowflake or PostgreSQL.
• Multi-tenant or customer-hosted (on-prem) deployments, ideally across AWS, Azure, and GCP.
• Docker, Kubernetes, and Terraform.
• Audit logging, data lineage, and feature-flag or controls systems.
• Auditable systems in regulated environments such as fintech, banking, or healthcare.
• Exposure to ML, statistical workflows, or LLM application patterns, enough to integrate them well.