Senior ML Engineer

Voodoo·France·Удалённо·3 нед. назад

Founded in 2013, Voodoo is a tech company that creates mobile games and apps with a mission to entertain the world. Gathering 800 employees, 7 billion downloads, and over 200 million active users, Voodoo is the #3 mobile publisher worldwide in terms of downloads after Google and Meta. Our portfolio includes chart-topping games like Mob Control and Block Jam, alongside popular apps such as BeReal and Wizz.

The Engineering & Data team builds innovative tech products and platforms to support the impressive growth of their gaming and consumer apps which allow Voodoo to stay at the forefront of the mobile industry.

Within the Data team, you’ll join the Ad-Network Team which is an autonomous squad of around 30 people. The team is composed of top-tier software engineers, infrastructure engineers, data engineers, mobile engineers, and data scientists (among which 3 Kaggle Masters). The goal of this team is to provide a way for Voodoo to monetize our inventory directly with advertising partners, and relies on advanced technological solutions to optimize advertising in a real-time bidding environment. It is a strategic topic with significant impact on the business.

This role can be done on fully remote (EMEA time-zone).

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.

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.

Experience with OpenRTB and real-time bidding systems.
Expertise in real-time data processing and event-driven architectures (e.g., Kafka, GCP Pub/Sub, Kinesis) for building responsive, low-latency systems.
Genuine interest in understanding the full machine learning lifecycle - from modelization and training to serving and monitoring .
Experience with model serving frameworks (Triton Inference Server, TorchServe, TF Serving, Ray Serve, KServe, or similar) - deploying, configuring, and operating model servers in production.
Exposure to data science workflows and understanding how data engineers and data scientists collaborate in productionizing ML models.
Contributions to open-source projects or active involvement in technical communities related to backend engineering or machine learning.