Machine Learning Scientist

2 weeks ago

india Yeah! Global Full time
Job Description

As a Machine Learning Scientist, your role involves developing ML systems that drive closed-loop automated decision-making for large enterprises. Our technology includes large transformer-based models that process multi-modal data, as well as reinforcement learning-based smart exploration/exploitation systems. Your responsibilities will span from initial exploratory/experimental models to scaled, production processes. While your work may begin with simple models, the ultimate objective is to push the boundaries of what is scientifically and technically achievable and share these advancements with the wider scientific community through publication and/or open-source contributions.

Required qualifications:

  • Experience in model training, deployment, and maintenance in a production environment.
  • Proficiency in deep learning and related frameworks (e.g., PyTorch, TensorFlow, DeepSpeed, Ray, PyTorch Lightning).
  • Experience with high-performance, large-scale ML systems.
  • Strong high-level programming skills and a dedication to good engineering practices, clean code, and scalability.
  • A profound passion for AI and building machine learning systems.
  • Interest in the entire ML stack, from initial R&D to deploying models into production.
  • Strong written and verbal communication skills for collaboration in a cross-functional team environment.

Nice to have:

  • Experience with active learning, reinforcement learning, causal inference, and/or online learning approaches.
  • Expertise in experimental design and Bayesian uncertainty estimation.

Requirements
Experience in model training, deployment, and maintenance in a production environment. Proficiency in deep learning and related frameworks (e.g., PyTorch, TensorFlow, DeepSpeed, Ray, PyTorch Lightning). Experience with high-performance, large-scale ML systems. Strong high-level programming skills and a dedication to good engineering practices, clean code, and scalability. A profound passion for AI and building machine learning systems. Interest in the entire ML stack, from initial R&D to deploying models into production. Strong written and verbal communication skills for collaboration in a cross-functional team environment. Preferred qualifications: Experience with active learning, reinforcement learning, causal inference, and/or online learning approaches. Expertise in experimental design and Bayesian uncertainty estimation.

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