Member of Technical Staff: Machine Learning Engineer (AI)

TL;DR

Member of Technical Staff: Machine Learning Engineer (AI): Translate cutting-edge research into production-ready machine learning systems for image and video processing with an accent on low-latency inference and scalable ML infrastructure. Focus on designing end-to-end ML pipelines, optimizing models with CNNs, Transformers, and deploying real-time systems.

Location: Fully remote, fully-distributed async-first culture with occasional team meetings a few times a year in London, UK or North America (LA, Toronto)

Company

Cutting-edge AI company building generational technology for media and entertainment.

What you will do

• Translate research into production ML systems and own full lifecycle: experimentation, training, evaluation, deployment
• Design, build, and optimize end-to-end ML models and pipelines for image/video processing
• Develop low-latency real-time inference systems and scalable ML infrastructure
• Rapidly prototype with open-source models and adapt for product needs
• Conduct experiments, analyze results, iterate for performance improvements
• Collaborate with researchers, product, engineering, design teams to deliver ML at scale

Requirements

• MS/PhD in Computer Science, Electrical Engineering or related
• Strong research experience, familiarity with top conferences (CVPR, ICCV, NeurIPS)
• 5+ years in Python; proficiency in Java, C++, or Scala
• Strong multi-threading, memory management; ML architectures (CNNs, RNNs/LSTM/GRU, Transformers)
• Experience with PyTorch or TensorFlow, end-to-end ML deployment for low-latency real-time apps
• Large-scale data with Spark; cloud deployment (AWS preferred); experiment tracking/ML workflows

Nice to have

• Low-level optimization, CUDA
• Productionizing/scaling ML models in real-world systems
• Open-source contributions
• MLOps tools, distributed training
• Relational databases (Postgres/MySQL)

Culture & Benefits

• Competitive salary and equity
• Private health coverage; pension (UK, Canada, US)
• Hardware setup of choice; stipends for phone, internet, meals
• Fully-distributed, async-first; high dedication like Olympic athletes, occasional late nights/weekends