Senior Applied ML Engineer [Remote]

Tasks

• As a Senior Applied ML Engineer, you will lead modeling and deployment for reinforcement learning/decision optimization, recommender systems, and computer vision. You’ll take systems from prototype to production—owning training, evaluation, deployment, and monitoring—while partnering with engineering and product to deliver measurable impact.
• Reinforcement learning / decision optimization
• Design environments and reward functions; build training and evaluation pipelines.
• Deploy policies safely with offline evaluation, guardrails, and gradual rollout strategies.
• Recommender systems
• Build candidate generation + ranking stacks (hybrid approaches, deep ranking, transformer-based recommenders where appropriate).
• Implement experimentation frameworks and measure impact with strong statistical discipline.
• Computer vision
• Develop CV pipelines (classification/detection/segmentation) with strong data strategy and metric-driven iteration.
• Optimize for real-world constraints (latency, throughput, accuracy, robustness).
• Productionization
• Package and deploy models as services; implement monitoring for performance, drift, and data quality.
• Collaborate with data/platform teams on reliable pipelines, feature computation, and scalable serving.
• Mentorship & technical leadership
• Raise engineering standards, review designs, and mentor team members.

Requirements

• 5+ years applied ML/ML engineering; 2+ years at senior/lead level.
• Strong PyTorch (preferred) or TensorFlow; ability to debug deep learning training and inference.
• Demonstrated experience in at least one of the following, with production ownership:
• Reinforcement learning / contextual bandits / sequential decisioning
• Recommender systems (ranking, retrieval, hybrid signals, evaluation)
• Computer vision (end-to-end training + deployment)
• Solid ML evaluation skills: offline metrics, online experimentation, bias/variance tradeoffs.
• Production engineering basics: containers, CI/CD, monitoring/alerting, model/version management.
• Strong understanding of algorithms, data structures, and performance optimization.
• Preferred / Nice-to-Have
• Distributed compute for ML: Ray, Spark, or similar large-scale processing/training.
• Advanced RL: multi-agent RL, distributed RL, offline RL, safe RL deployment patterns.
• Specialized recommender experience: two-tower retrieval, transformer recommenders, real-time ranking.
• CV at scale: efficient training, augmentation strategy, edge/real-time inference optimization.
• Feature stores, streaming/event-driven ML pipelines, and near-real-time decisioning.
• Strong SRE mindset for ML services (SLOs, dashboards, incident playbooks).

What we offer

• exciting, diverse AI projects
• end-to-end project responsibilities
• competitive salary
• opportunity to deepen professional knowledge across multiple domains (DL, LLM, CV)
• flexible, fully remote work opportunity