Senior Machine Learning Engineer, AI Platform

Das ist der Job

Work with a wide range of datasets—including image, video, audio, text, and structured performance data.

Darum lohnt es sich

Be a core contributor to the team’s workflows, actively participating in work planning, retrospectives, and overall team improvement. Hands‑on experience with modern ML frameworks (PyTorch, TensorFlow).

Benefits • Inclusive global culture with over 750 employees across 24 locations in 13 countries.• Global impact and opportunity to influence customers’ success and business growth.• Focus on wellbeing with healthcare packages, mental health services, paid holidays, and family leave.• Comprehensive rewards including equity options, performance‑based rewards, competitive compensation, and career development opportunities.• Flexible hybrid workplace with office collaboration, remote work, and the option to work abroad for up to 30 days annually. #J-18808-Ljbffr Responsibilities Build ML‑based software systems that enable users to craft great advertising experiences and campaigns.

Contribute to and strengthen the company’s MLOps and data platform, collaborating with senior ML engineers, data scientists, and software engineers to productionize AI/ML applications reliably and efficiently. Impact the daily operations of hundreds of advertisers using the platform.

Collaborate with stakeholders (product, engineering, infrastructure) to translate business and product needs into viable ML solutions. Enhance soft skills through close cooperation with colleagues and customers, including knowledge sharing, productive meetings, pair programming, and debugging.

Keep up to date with the latest machine‑learning innovations, especially in generative AI, computer vision, natural language processing, and explainability.

Qualifications 5+ years of experience developing and deploying production‑quality software. 2+ years of experience delivering software services powered by machine learning. 2+ years of experience working with cloud infrastructure such as AWS or GCP. Fluent in Python; experience with C++ or Java is a plus.

Knowledge of MLOps concepts and tools (MLflow, Kubeflow). Experience in feature engineering, model evaluation, diagnostics, and monitoring. Strong foundation in linear algebra, statistics, and calculus. Experience building scalable data pipelines for ML‑based data processing. Experience developing and maintaining services under SLA.

Analytical mindset with a problem‑solving approach. Passion for solving customer challenges quickly while making thoughtful architectural decisions. Adaptability and resilience when facing new or ambiguous challenges. Strong written and verbal communication skills in English. M.Sc. in a relevant field is preferred.

Ability and willingness to work in a hybrid capacity from the office 3 days a week.