Áttekintés
We are currently looking for a Senior Applied ML Engineer on behalf of one of our partner companies. Our partner is an 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.
Részletes leírás
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.
- 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).
- 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