You will integrate a small, high-ownership squad where you own the full lifecycle
design, implement, deploy, monitor, and iterate
with direct impact on revenue-critical systems processing millions of bid requests per second.
Lead the design and architecture of backend services that power real-time model inference and bidding decisions for our OpenRTB platform.
Collaborate with data scientists and machine learning engineers to deploy, monitor, and optimize ML models that influence real-time bidding strategies, pricing decisions, and targeting
including potentially developing proprietary models in-house.
Oversee the development of A/B testing frameworks and ensure the seamless integration of experimentation tools into our platform for continuous model and bidding optimization.
Ensure that all backend services are high-performance, low-latency, and scalable, capable of handling large data volumes (millions of bidding events per second).
Set best practices for architecture, API design, and distributed systems to ensure robust and maintainable systems at scale.
Work with the cloud infrastructure teams to ensure efficient deployment, scaling, and monitoring of backend services using Kubernetes, Docker, and CI/CD pipelines.
Work closely with product managers to define and implement new features, optimizations, and improvements to the bidding and model inference system.
Lead efforts to optimize performance and cost-efficiency across the backend infrastructure, ensuring that the system can scale effectively with increasing traffic and data.
Continuously monitor the system’s performance, perform post-deployment analysis, and make improvements based on real-world usage and A/B test results.
Requirements
7+ years of experience in backend engineering, with a strong focus on designing and building scalable, high-performance systems.
Well-versed with machine learning and deploying models for inference in production.
Extensive experience in distributed systems, microservices, and API design using Python.
Hands-on experience with observability tools (e.g., Prometheus, Grafana) for metrics collection, log aggregation, and system monitoring.
Understanding of A/B testing concept and experience integrating them into backend systems for experimentation and optimization.
Proven ability to design, build, and manage cloud infrastructure using Kubernetes, Docker, and cloud-native tooling. Experience with other cloud providers is welcome, but AWS is preferred as it is our primary platform.
Solid experience with CI/CD pipelines, and infrastructure automation tools (e.g., Terraform).
A focus on building reliable, maintainable, and scalable systems, with experience in performance tuning and cost optimization.
Strong problem-solving skills, quality ownership and autonomy: able to take a task from design to production end-to-end
writing contract tests for upstream/downstream interfaces, verifying changes in staging, and treating CI as necessary but not sufficient.
Tech Stack
AWS
Cloud
Distributed Systems
Docker
Grafana
Kubernetes
Microservices
Prometheus
Python
Terraform
Benefits
Competitive salary upon experience
Swile Lunch voucher
Gymlib (100% borne by Voodoo)
Premium healthcare coverage SideCare, for your family is 100% borne by Voodoo
Child day care facilities (Les Petits Chaperons rouges)