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Pozíció leírása / Job description

Our partner is a fast-growing, innovation-driven company building and deploying AI solutions across Space, Manufacturing, AdTech, and FinTech. They combine state-of-the-art research with robust engineering to solve real-world problems at production scale.

  • RL & Decision Optimization: Design environments and reward functions; deploy policies safely using offline evaluation and gradual rollouts.

  • Recommender Systems: Build hybrid candidate generation and ranking stacks; drive impact through rigorous statistical experimentation.

  • Computer Vision: Develop high-performance CV pipelines (classification/detection) optimized for real-world latency and robustness.

  • Productionization: Deploy models as scalable services; implement monitoring for drift, data quality, and automated feature pipelines.

  • Technical Leadership: Raise engineering standards through design reviews, mentorship, and cross-team collaboration

Elvárások / Requirements

  • Senior ML Expertise: 5+ years in applied ML/MLE (2+ years at senior/lead level) with mastery of PyTorch or TensorFlow for deep learning debugging and inference.

  • Specialized Domain Ownership: Proven production experience in at least one area: RL/Contextual Bandits, Recommender Systems (Ranking/Retrieval), or Computer Vision.

  • Core Engineering: Strong command of algorithms, data structures, and performance optimization alongside MLOps basics (Containers, CI/CD, Monitoring).

  • Advanced ML Ops & Scaling: Experience with distributed compute (Ray/Spark), feature stores, and streaming/event-driven pipelines for real-time decisioning.

  • Evaluation & Reliability: Expertise in offline/online experimentation and a strong SRE mindset for maintaining ML service SLOs and incident readiness.

Amit nyújtunk / Benefits

  • Career Growth

  • Collaborative Team

  • Exciting Projects

  • Remote Work

Randstad Hungária Kft. logó
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Senior Applied ML Engineer (Reinforcement Learning/Recommenders/Computer Vision)

Budapest
Full time

Published on 23.05.2026

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