MLOps/Platform Engineer

About Codeway

Codeway is a technology company building AI-powered mobile apps used by over 400 million people worldwide.

Since 2020, we’ve launched 60+ products across photo editing, productivity, entertainment, and more. Many of them lead their categories. Our standout apps include Cleanup, Retake, Learna, and Chat & Ask AI.

With a deep focus on AI research and proprietary technologies, we build experiences that define new standards in mobile. We don’t just follow trends. We set new standards for what’s possible on mobile. In 2024, we became the most downloaded app publisher on iOS globally.

Behind it all is our team of 300+ people who bring curiosity, clarity, and ownership to everything we build. Recognized as Turkey’s #1 LinkedIn Top Startup and a certified Great Place to Work across Europe and the tech industry, Codeway is designed to attract, grow, and empower top talent.

‍POSITION

‍Quality Assurance plays a huge role in our product development process. Here at Codeway, QA is not only about running tests, it is about taking full ownership of a product’s success right before it meets millions of users around the world. Sounds exciting? Keep reading!

We are looking for a Quality Assurance (QA) Engineer to develop and execute exploratory and automated tests to ensure the quality of our mobile products in a multitude of categories.

The QA technician role plays a significant part in Codeway’s product development process. Our ideal candidate will be responsible for conducting tests before product launches to ensure software runs smoothly and meets user needs, while being cost-effective. QA engineer responsibilities include designing and implementing tests, debugging and defining corrective actions. You will also review system requirements and track quality assurance metrics (e.g. defect densities and open defect counts.) Ultimately, you should monitor all stages of software development to identify and resolve system malfunctions to meet quality standards. If you hold an engineering background and enjoy providing end-to-end solutions to software quality problems, we’d like to meet you!

‍WHAT YOU'LL BE DOING

• Design, build, and operate Kubernetes-based GPU infrastructure for AI/ML research and production workloads.
• Provide self-service deployment capabilities to AI researchers, data engineers, and product teams.
•  Implement and manage continuous delivery pipelines using Kubernetes-native deployment tools (e.g., ArgoCD).
• Configure and optimize GPU workloads (CUDA, drivers, device plugins, and scheduling strategies).
•  Implement autoscaling strategies for both CPU and GPU workloads on Kubernetes clusters.
• Partner with data teams to ensure platforms are tailored to data pipelines, training workflows, and ML model lifecycle needs.
• Contribute to the adoption of ML orchestration and experiment tracking platforms (e.g., Kubeflow, MLflow, ClearML, Anyscale).
• Support notebook environments for experimentation (e.g., JupyterHub).
• Act as a platform engineering lead for AI/ML workloads—adopting the best practices, reliability, and cost-efficiency.

WHAT YOU’LL BRING?

•  Strong hands-on experience with Kubernetes (designing, operating, and troubleshooting clusters).
• Proven experience deploying workloads with ArgoCD or similar GitOps tools.
•  Exposure to ML platforms such as Kubeflow, MLflow, ClearML, or Anyscale.
• Prior experience working with data engineering teams or ML model lifecycle management.
• Experience enabling notebook-based workflows (JupyterHub, SageMaker Studio, etc.).
• Knowledge of cloud platforms (AWS, GCP, or Azure) and their GPU offerings.
• Strong collaboration and communication skills—able to partner effectively with researchers, engineers, and product teams.

NICE TO HAVES?

•  Hands-on experience with GPU configuration, CUDA, or NVIDIA ecosystem tools (NCCL, Triton Inference Server).
• Knowledge of autoscaling patterns for AI/ML workloads on Kubernetes (KEDA, HPA, VPA, custom controllers).
•  Experience with observability and monitoring tools (Prometheus, Grafana, Loki, ELK, etc.).
• Solid understanding of containerization (Docker, OCI) and CI/CD pipelines.
• Familiarity with infrastructure as code (Terraform, Helm, Kustomize).
• Background in platform engineering, SRE, or DevOps with a focus on enabling other teams.

THE RECRUITING PROCESS

We are committed to keeping our recruitment process short and transparent. Here’s how it looks like:

1. Application: Send us your CV or LinkedIn profile. You can also just write a few words about yourself.

2. Case Study: We might send you a task to solve.

3. Talent & Culture Interview: Let’s talk about your experience & expectations and see what we can achieve together.

4. Technical Interview: You will meet with future team lead to go over the case and your technical capabilities in detail.

5.  Reference Check: We will contact your references to gain insights into your past experiences and work ethic.

6. Welcome Aboard! You are now a part of the team.