SDE-1
Confirmed on the employer's own hiring board on Nov 2, 2026. First seen by Alion on Oct 8, 2026. Zenskar scores B on the Alion truth index.
About Zenskar
Zenskar is an AI-native revenue automation platform built to handle real-world complexity.
It sits between your CRM and ERP automating everything in between: billing, revenue recognition, collections, usage metering, and analytics. Across any pricing model, any entity structure, any currency.
Our vision: Zero-Touch Finance. Agents execute. Humans supervise. Finance teams set the rules. Zenskar runs them.
Founded by Apurv Bansal and Saurabh Agarwal, second-time founders whose previous startups were acquired by Snapdeal and Gaana. The team brings experience from Google, Deutsche Bank, Elevation Capital, IIT Bombay, IIT Delhi, and Harvard Business School.
We have 5x'd revenue in the past year. We are default alive.
Funding
We raised $15M in Series A funding in April 2026, led by Susquehanna Venture Capital, Bessemer Venture Partners, Shine Capital, and Rho, with participation from Rocketship, J-Ventures, Future Back Ventures by Bain & Company, and Converge.
The funding is being used to expand our Agents Marketplace and scale operations, not headcount.
https://youtu.be/jRb2ybvrSwU?si=vuLLgUY_MFdRzDMl
See how Zenskar automates the full order-to-cash cycle, from contract to cash,
without spreadsheets, workarounds, or engineering tickets.*
The Problem We're Solving
Finance teams aren't struggling because they lack AI tools. They're struggling because the systems underneath those tools were built for a simpler world. These systems label real-world complexity as edge cases and force teams into costly, error-prone workarounds: revenue leakage, delayed collections, audit risk, and finance teams buried in grunt work.
Bolting AI onto these broken foundations is as good as a Ferrari engine on a horse-drawn carriage. Zenskar is purpose-built from the ground up: the architecture handles complexity natively, so AI actually works.
It combines foundational flexibility + deterministic calculation + purpose-built AI agents to deliver end-to-end automation across the order-to-cash cycle - without workarounds, without engineering tickets.
The B2B billing and revenue automation market is $2B today, projected to reach $10B by 2030.
What Customers Say
"We're saving 200+ hours/quarter on invoicing and receivables by completely automating our recurring billing."- Noy Kalansky, Finance Controller, Pontera
"Zenskar automates revenue recognition accurately for our value-based billing: agents reducing manual hours by 70%."
- Matt Barnard, VP Finance, Vertice
"Zenskar’s agents automated 90% of our billing, integrated with our CRM, and accelerated revenue collection by a month."
- Ming Lui, VP Finance, Yembo
"Sardine had spent 4 years running billing in-house for high-volume, usage-based pricing. Zenskar took care of it all."
- Sardine team
"We launched our product 4 months faster instead of building an in-house system for our usage-based pricing."
- Kshitij Gupta, CEO, 100ms
About the role
We’re looking for an early-career engineer who enjoys building things end to end.
You might build an API in the morning, wire it into a product workflow in the afternoon, and spend the next day figuring out why one very determined edge case refuses to cooperate.
This is a full-stack role, but not one where “full stack” means changing button colours and occasionally calling an API. You’ll work across product UI, backend services, data models and integrations. You can be stronger on one side: we don’t expect perfect symmetry - but you should be excited to learn and work across both.
Our backend is primarily Python and Postgres on AWS. On the frontend, you’ll work with a modern JavaScript/TypeScript application. Familiarity with our exact stack helps, but it is not a requirement.
Also: you do not need to know billing or accounting before joining. You will learn. Probably more than you ever planned to.
What you'll walk into
This is software that moves money, so correctness matters. A tiny-looking edge case can create a very real invoice, and customers tend to notice those.
At the same time, the product needs to feel simple. Finance teams should not need to understand our database schema-or summon an engineer-to complete everyday work.
That combination creates interesting full-stack problems:
- Turning complex billing rules into workflows that are understandable in the UI.
- Building APIs that remain predictable when customer configurations become complicated.
- Showing useful errors instead of “Something went wrong. Please try again.”
- Handling retries, duplicate requests and partial failures safely.
- Making large datasets and long-running operations feel responsive.
- Giving users visibility into what our AI agents did, why they did it and what needs human attention.
You won’t spend your first three months watching onboarding videos. You’ll pair with the team, read real code and start shipping carefully scoped changes.
Projects you might work on
The exact project will depend on product priorities, but early SDE1 projects could look like:
- Make a product workflow truly end to end: Build or improve a workflow spanning a frontend form, API validation, persistence and the final success/error experience.
- For example: configuring a pricing rule, creating a customer, reviewing an invoice adjustment or setting up a usage alert.
- Make failures understandable: Improve a workflow where backend validation is technically correct but the user has no idea what to do next. You might introduce structured errors in the API, map them to the right UI state and add enough observability to understand how often the failure occurs.
- Improve an integration experience: Work on a CRM, ERP or payment integration: status tracking, retry behavior, duplicate handling, failure visibility and the UI through which a user understands what happened.
- Make a slow screen boringly fast: Trace a slow product workflow from browser to database. Improve the query or API, introduce pagination where needed and make loading behavior sensible in the UI.
- Write an AI agent to solve a business usecase: Write an AI agent on our internal platform to help solve customers’ usecases.
Your first 30 days
The goal of the first month is not to know everything. It is to become comfortable enough with one part of the system to make small useful, safe changes.
By the end of your first 30 days, we expect you to have:
- Set up the application locally and understood how code moves from your laptop to production.
- Learned the basic journey from contract and usage data to invoices and revenue workflows.
- Traced at least one product flow from the UI through the API and database.
- Shipped a few small production changes-likely a bug fix or contained product improvement.
- Written or updated tests for the behavior you changed.
- Participated in code reviews and acted on feedback.
- Debugged one real issue alongside another engineer.
- Demoed something you shipped to the team.
- Learned how we use logs, metrics and other tools to verify that a change is healthy.
It is completely fine to ask a lot of questions. We would much rather answer an early question than investigate a confidently shipped mystery later.
By 90 days
By the end of your first 90 days, you should be able to own a small, well-scoped feature from discussion to production.
That means you can:
- Clarify the expected behavior and identify important edge cases.
- Break the work into manageable pieces.
- Make the necessary frontend, API and data-model changes.
- Handle loading, empty, validation, permission and failure states.
- Write useful tests without being reminded at the end.
- Work with product and design when the expected experience is unclear.
- Use code review feedback to improve the solution rather than only patching comments.
- Deploy the feature and verify that it behaves correctly in production.
- Investigate a production issue using logs and the codebase, with help when needed.
- Explain what you built, why you built it that way and what you would improve next.
You will not be expected to design the next five years of Zenskar’s architecture. You will be expected to take increasing ownership of your corner of it.
A good 90-day outcome is not “never needed help.” It is “asked for help at the right time, learned quickly and became noticeably more independent.”
Who you are
- You have 1-3 years of professional software development experience. Substantial internships and strong personal or open-source projects count.
- You have built at least one non-trivial application or feature and can explain what you personally contributed.
- You are comfortable programming in at least one mainstream language and are willing to work in Python.
- You understand basic web concepts: HTTP, APIs, databases, validation and application state.
- You can write ordinary, readable code using common data structures and control flow.
- You enjoy figuring things out, but you also know when to ask for help.
- You can explain your thinking without hiding behind framework terminology.
- You care whether the feature works for the user-not only whether your pull request was merged.
Good to have
None of these are mandatory:
- Python, Postgres or SQL experience.
- Experience with a modern JavaScript or TypeScript frontend framework.
- Building and consuming REST APIs.
- Exposure to AWS, Docker, CI/CD or background jobs.
- Experience at a startup or on a small product team.
- A habit of writing unit and integration tests.
- Personal projects that have real users-even if the number of users is your five very patient friends.
- Experience using AI coding tools without outsourcing your understanding to them.
- Any exposure to billing, payments, accounting or fintech.
Location
- Hybrid - 3 days per week
- Office Location: Indiranagar, Bengaluru.
- Address: 3rd Floor, A wing No 1, Carlton Towers, HAL Old Airport Rd, HAL 2nd Stage, Indiranagar, Bengaluru, Karnataka 560008.
Interview process
Our interviews are practical and evidence-based. No single round exceeds 60 minutes, and we are not interested in testing whether you memorised obscure Python behavior or 200 LeetCode problems.
- R0, Introductory conversation: A short conversation about something you built, what you personally owned, what went wrong and what you learned. We’ll also align on the role, location and compensation.
- R1, Practical coding and debugging: You’ll work in a small Python codebase with supplied tests. The goal is to understand the requirement, write working code and debug failures-not solve an algorithm puzzle.
- R2, Code-first design: You’ll implement a small component with a few business rules and then adapt it when the requirement changes.
- R3, Hiring Manager: A discussion about projects, learning, feedback, mistakes, ownership and the kind of environment in which you do your best work.
During technical rounds, documentation and syntax search are allowed. AI can help with syntax or a small isolated utility, but it cannot be used to solve or debug the interview problem for you.
We’ll explain the rules clearly before starting. No surprise gotchas.