Engineering, Platform
Pleasanton, CA (Hybrid) (USA)
Full-time
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, including ERPs, CRMs, billing platforms, and 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
The Staff Backend Engineer owns the application and API services layer: the services through which everything the platform computes actually reaches a person.
Multi-tenant APIs, authentication and authorization, and the review and approval workflows where a controller accepts a result, challenges it, or sends it back. The computation layer produces the numbers. This layer is where a finance team sees them, interrogates them, approves them, and acts on them.
That means your services carry the same correctness and auditability requirements as the computation beneath them, plus the access control and tenancy guarantees that let a Fortune 500 finance organization trust the platform with their close cycle.
Every action taken through your services is part of an audit trail that sits next to externally reported financials. Who approved what, when, on the basis of which underlying result, and what changed afterward. That is not logging you add later. It is a product requirement, and it shapes how the services are designed from the first commit.
This is hands-on and high-ownership. You own major services end to end, set backend patterns alongside the Senior Staff engineers, and partner with data, AI, and product on the contracts between layers.
Key Responsibilities
1. Own the multi-tenant APIs through which the platform's results are consumed. Design the service contracts between the application layer and the computation, data, and AI layers, and own the versioning discipline that lets those contracts evolve without breaking consumers who cannot all move at once.
2. Build the review, approval, and exception-handling workflows finance teams operate in. This is where the product either earns trust or loses it. A controller who cannot see why a number is what it is will not approve it, and will not use the platform twice.
3. Build and maintain multi-tenant isolation at the service layer. Own authentication, authorization, and role-based access across the application surface. Cross-tenant leakage should be impossible by construction rather than prevented by convention.
4. Build the audit trail. Who saw what, who approved what, on the basis of which result. Guarantee that every user-facing figure links back to its computation and its source, and support the enterprise security and compliance reviews Fortune 500 finance customers run before they buy.
5. Build for a multi-cloud-ready, multi-tenant-ready, customer-hosted deployment model. Services that run inside a customer's environment with strong isolation and no data egress.
6. Own performance, reliability, and cost of the services you build, including observability and operational health. You should be able to answer why a request is slow rather than guessing.
7. Set backend patterns, review designs, and mentor mid and senior engineers. Partner with data, AI, and product on cross-layer service design.
A note on accountability: this role owns the application and API services, tenancy and access control, and the audit trail. It does not own the core computation layer, the data foundation, the AI layer, or product prioritization.
Requirements
Seven or more years in backend forward deployment engineering, with significant Staff-level time owning complex production services. Owning them, including what happens when they fail.
- Demonstrated ownership of services in a multi-cloud SaaS environment, and a clear account of how you guaranteed isolation rather than assumed it.
- An obsession with correctness and traceability. You treat "who did what, when, and on what basis" as a first-class requirement rather than an audit checkbox.
- Strong API and service design, including versioning, contracts, and backward compatibility. You have evolved an interface other teams depended on without breaking them.
- Practical security engineering: authentication, authorization, and tenant isolation. Not theory.
- Python
- FastAPI, Django, or Flask; PostgreSQL and Redis;
- REST API design; Docker, Kubernetes, and Terraform
- SQL against Snowflake or PostgreSQL.
- Nice to Have
- Finance, accounting, or fintech domain, or a genuine willingness to learn how financial statements are built and reconciled.
- Multi-tenant, customer-hosted deployment at scale.
- Supporting SOC 2 or ISO 27001 review.
- Building auditable systems in a regulated environment.
- Exposure to ML or LLM application patterns, enough to integrate them well.
Benefits
- Health and wellbeing. Medical, dental, and vision insurance, with MeeruAI covering 50 % of employee premiums
- Flexible spending accounts, health savings accounts with a company contribution
- Unlimited PTO and 12 paid holidays.
- 401(k) with company matching
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“What changed, why did it happen, and what should we do next?” Every finance team gets asked. Too often, the answer is: “I’ll get back to you.” Not because finance doesn’t understand the business. Because the answer is scattered across ERP systems, planning tools, spreadsheets—and, most importantly, in the heads of the people who understand how the business actually works. The context that explains the numbers was never captured. That’s why finance AI is not simply a model problem. It is a context problem. Generating an explanation is the easy part. Getting it right requires an understanding of the company’s data, definitions, operating model, history, and decision-making. MeeruAI builds AI workbenches for the Office of the CFO that carry that context into the work. The Performance AI Workbench traces changes in revenue, margin, and financial performance back to the drivers that moved them, generates the narrative, and surfaces the next best actions. The Close AI Workbench orchestrates close activities, surfaces blockers, supports reconciliations and approvals, and gives controllers a real-time view of whether the books will close on time. Both workbenches sit on a shared foundation that includes the Finance Command Center, a contextual data model, and purpose-built finance agents. MeeruAI connects to the systems companies already use and understands the business in its own language. Controls, approvals, and traceability are built in from the start. We built MeeruAI after decades spent working with finance and technology leaders and watching teams repeatedly reconstruct the same business context by hand. Today, three Fortune 500 companies are working with us as paying design partners to build a better way. Finance that never says, “I’ll get back to you.” Explain every variance. Act with confidence. Close faster.
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