The Automation Platform team plays a critical role in making testing faster, easier, and more efficient while enhancing overall product quality. As a Machine Learning Engineer, you will lead a team responsible for building and evolving the testing platform that engineering teams rely on to validate their products. Your mission is to lay the foundations for how Agentic Testing can enhance our testing suite for mobile application development. This job will design, develop, and implement machine learning models and algorithms to solve complex problems. You will work closely with data scientists, software engineers, and product teams to enhance services through innovative AI/ML solutions.

Essential Responsibilities:

• Develop and optimize machine learning models for various applications.
• Preprocess and analyze large datasets to extract meaningful insights.
• Deploy ML solutions into production environments using appropriate tools and frameworks.
• Collaborate with cross-functional teams to integrate ML models into products and services.
• Monitor and evaluate the performance of deployed models.

Minimum Qualifications:

• Minimum of 5 years of relevant work experience and a Bachelor's degree or equivalent experience.
• Experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn.
• Familiarity with cloud platforms (AWS, Azure, GCP) and tools for data processing and model deployment.
• Several years of experience in designing, implementing, and deploying machine learning models.

• 8 years of industry experience with deep learning architectures, building, fine-tuning and deploying ML models in production.
   
• Strong proficiency in Python, Scala, or other programming languages.
  Familiarity with ML frameworks (TensorFlow, PyTorch, XGBoost, etc.).
   
• Experience working with large datasets, data processing pipelines (e.g., Dataflow, Spark, Flink), and scalable architectures.
   
• Strong communication and collaboration skills, with the ability to work effectively across teams and contribute to a high-performing engineering culture.
   
• Experience working on search or recommendation systems at scale.
   
• Familiarity with A/B testing and experimentation methodologies for search relevance improvement.