Senior Applied ML Engineer (Reinforcement Learning/Recommenders/Computer Vision)

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