Senior Backend Engineer, Monte Carlo
$180-230k
Offers Equity
Data observability platform
Be an early applicant
Data observability platform
501-1000 employees
Be an early applicant
$180-230k
Offers Equity
501-1000 employees
Company mission
To accelerate the world’s adoption of data by reducing data downtime.
Role
Who you are
- Backend depth. 5+ years shipping production backend services. Strong Python or an equivalent backend language, and real experience designing, running, and debugging APIs and services under load
- Distributed systems. You've built and scaled distributed architectures yourself and know the tradeoffs around reliability, consistency, and observability from running them in production
- 0-to-1 ownership. You've taken ambiguous problems from a blank page to a deployed product: prototype, architecture, build, testing, and deploy. You move with urgency and treat outcomes as yours
- Data and cloud. Experience with data pipelines or data-heavy systems on AWS and cloud-native services. PySpark and ML platform experience are a plus
- Fullstack range. Frontend experience, ideally React, so you can ship the whole feature. Experience with agentic or LLM-powered systems is a strong plus
What the job involves
- We're hiring a Senior Fullstack Engineer to build the core of our agent trust platform: the backend services, distributed systems, and agentic workflows that let enterprises monitor and trust AI in production
- You'll get a problem statement, not a spec, and take it from prototype to architecture to a tested, deployed product. The role is mostly backend, and you'll ship the React surfaces that go with it when the work calls for it
- Take vague problem statements to production: prototype fast, pick the architecture, build it, test it, deploy it, and own it after launch
- Build and run production-grade backend services and APIs in Python that power Monte Carlo's core platform and agentic systems
- Design and scale distributed systems that stay reliable, observable, and fast as customer data and agent volume grow
- Start with simple, flexible designs and evolve them as the product and company scale, without over-building up front
- Build and maintain data pipelines behind analytics, ML, and customer-facing features
- Work with product, ML, and infrastructure partners to ship customer value, and build React front ends where they're needed to finish the job
Our take
Dozens of billion-dollar companies have emerged with a shared focus on easing the burden on engineering teams constantly battling to guarantee reliability in their products. For example, New Relic revolutionised application observability. In the same way, Monte Carlo is tackling data observability.
The service pulls information from metadata log metrics to help teams understand their data health, and if something breaks, the product will send proactive notifications. The product is an off-the-shelf SaaS offering that customers can integrate and customise over time.
The company has won some major clients including big tech companies like Affirm and Intuit, but also JetBlue, PepsiCo, CNN, The New York Times, Asics, Rivian and others from many sectors. Its goal is to help companies reduce data downtime and the time data engineers must spend on tedious data quality tasks.
'Data Observability’ became a point of furore in venture capital in 2022. For example, in the span of one week, just three companies raised more than $400 million between them. Staying at the forefront of this industry is an interesting challenge for Monte Carlo, and it met it in 2024 with product enhancements that include new GenAI capabilities for its platform.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
32% employee growth in 12 months
Company
Funding (last 2 of 4 rounds)
May 2022
$135m
SERIES D
Aug 2021
$60m
SERIES C
Total funding: $236m
Company HQ
Dolores Heights, San Francisco, CA
Leadership
Barr Moses
(Co-Founder & CEO)Previously VP of Customer Success Operations for Gainsight. Originally trained as a Management Consultant with Bain & Company. Also attended Stanford where they studied Mathematics and Computer Science.
Lior Gavish
(Co-Founder & CTO)Previously SVP of Engineering for another tech startup, Barracuda. Also previously worked for PayPal and holds an MBA from Stanford University.
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