Senior Applied ML Engineer (Reinforcement Learning/Recommenders/Computer Vision)
Confirmed on the employer's own hiring board on Sep 25, 2026. First seen by Alion on Jul 8, 2026.
Career Growth
Collaborative Team
Exciting Projects
Remote Work
Responsibilities
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
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.