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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.
Job Responsibility:
Design environments and reward functions
deploy policies safely using offline evaluation and gradual rollouts.
Build hybrid candidate generation and ranking stacks
drive impact through rigorous statistical experimentation.
Develop high-performance CV pipelines (classification/detection) optimized for real-world latency and robustness.
Deploy models as scalable services
implement monitoring for drift, data quality, and automated feature pipelines.
Raise engineering standards through design reviews, mentorship, and cross-team collaboration
Requirements:
5+ years in applied ML/MLE (2+ years at senior/lead level) with mastery of PyTorch or TensorFlow for deep learning debugging and inference.
Proven production experience in at least one area: RL/Contextual Bandits, Recommender Systems (Ranking/Retrieval), or Computer Vision.
Strong command of algorithms, data structures, and performance optimization alongside MLOps basics (Containers, CI/CD, Monitoring).
Experience with distributed compute (Ray/Spark), feature stores, and streaming/event-driven pipelines for real-time decisioning.
Expertise in offline/online experimentation and a strong SRE mindset for maintaining ML service SLOs and incident readiness.