Member of Technical Staff - Backend

Company Overview

Model ML is the AI workflow builder transforming how major financial institutions produce and validate client-ready work. Model ML converts complex, manual processes into fully automated AI systems that scale across global teams. In under a year, Model ML has become one of the fastest growing enterprise AI platforms worldwide and recently closed a $75 million Series A, one of the largest fintech Series A rounds ever. The round was backed by FT Partners, Y Combinator, LocalGlobe, QED, 13books, and other top global investors, bringing total funding to $90 million.

Job Description

As a Senior Backend Engineer at Model ML, you'll be at the forefront of building and scaling the infrastructure that powers our our product. You'll design and implement robust, high-performance backend systems that handle complex data pipelines, enable seamless AI model deployment, and ensure enterprise-grade security and compliance for our financial services clients. Working closely with machine learning engineers, product teams, and infrastructure specialists, you'll architect scalable solutions that process sensitive financial data with precision and reliability.

This role offers the opportunity to tackle unique technical challenges at the intersection of AI and finance, where your work will directly impact how financial institutions leverage artificial intelligence to transform their operations. You'll drive technical decisions, mentor junior engineers, and help shape the engineering culture as we scale our platform to serve the world's leading financial organisations.

Responsibilities

Design, develop, and maintain scalable backend services and APIs that power Model ML's AI workspace platform

Build and optimize data pipelines for processing large-scale financial datasets with high accuracy and performance

Implement robust security measures and ensure compliance with financial industry regulations (SOC 2, GDPR, FCA requirements)

Collaborate with ML engineers to productionize machine learning models and integrate them into backend systems

Optimize database schemas and queries for high-throughput, low-latency operations across distributed systems

Lead technical design reviews and mentor junior and mid-level engineers

Monitor system performance, troubleshoot production issues, and implement solutions to improve reliability and uptime

Contribute to engineering best practices, code quality standards, and technical documentation

What you can expect

It won't be easy; in fact, it will be very hard.

BUT, it will be a lot of fun.

You need to be comfortable with being uncomfortable; timelines will change, priorities will most likely shift.

Requirements

7+ years of professional backend engineering experience with a proven track record of building and scaling production systems

Expert-level proficiency in Python and modern web frameworks, particularly FastAPI (or similar frameworks like Flask or Django)

Deep understanding of relational databases, especially PostgreSQL—including query optimisation, indexing strategies, and performance tuning

Proven experience scaling databases

Strong knowledge of caching strategies and in-memory data stores, particularly Redis

Hands-on experience with asynchronous task processing using Celery or equivalent distributed task queues

Proficiency with message brokers and event-driven architectures (Azure Service Bus, RabbitMQ, Kafka, or similar)

Solid understanding of RESTful API design principles and microservices architecture patterns

Experience with cloud platforms (Azure preferred; AWS or GCP acceptable) and containerization technologies (Docker, Kubernetes)

Strong knowledge of security best practices, authentication/authorization mechanisms, and data encryption

Familiarity with CI/CD pipelines, automated testing, and version control systems (Git)

Excellent problem-solving skills with the ability to debug complex distributed systems

Strong communication skills and experience collaborating with cross-functional teams

Bachelor's degree in Computer Science, Engineering, or equivalent practical experience

What We Offer

Competitive salary + equity

Performance-based incentives

Opportunity to be instrumental in our expansion into the market

Supportive and innovative work environment

About the interview

Our Process: We're very conscious of everyone's time, so we want to make the process as efficient as possible.

Call 1: 30-minute intro call with our Talent Acquisition team

Call 2: 30-minute technical screen

Call 3: 20-minute systems design deep-dive

Call 4: Onsite interview with Engineering Leadership