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Machine Learning Engineer
Quantiphi is an award-winning Applied AI and Big Data software and services company, driven by a deep desire to solve transformational problems at the heart of businesses. Our signature approach combines groundbreaking machine-learning research with disciplined cloud and data-engineering practices to create breakthrough impact at unprecedented speed.
Some company highlights:
• Quantiphi has seen 2.5x growth YoY since its inception in 2013.
• Winner of the "Machine Learning Partner of the year" award from Google for two consecutive years - 2017 and 2018.
• Winner of the "Social Impact Partner of the year" award from Google for 2019.
• Headquartered in Boston, with 700+ data science professionals across different offices.
For more details, please visit us on https://quantiphi.com/
Work Experience(yrs): 5+ yrs
Role: Associate Technical Architect - Machine Learning
Work location: Mumbai/Bengaluru/Trivandrum
A Machine Learning/Deep Learning Engineer who has experience with scaling model training from one GPU to multiple GPUs. Must have familiarity with popular Deep Learning Frameworks and distributed training paradigms to help customers with optimally using their hardware.
- Hands-on experience in implementing Deep Learning based model training and validation pipelines using either Tensorflow or Pytorch. Pytorch preferred.
- Experience with Multi-GPU training(Dask ML) - Understanding of Data Parallelism and Model Parallelism
- Understanding of algorithmic and engineering challenges, benefits, bottlenecks and trade-offs when scaling to larger number of GPUs
- Familiarity with Docker containers
- Strong hands-on experience coding with Python
- ML and SQL knowledge
- Experience with Dataframes and Arrays on GPUs
- Strong experience of working on projects involving concepts in Deep NLP, language models and other downstream tasks
Ways to stand out from the crowd:
- Background in biochemistry
- API Building using frameworks such as Flask or web development
- Spark 3.0 - C++, CUDA & Cython
- R, Matlab knowledge
- Experience with Multi-Node Training
- Inference on large clusters
- Knowledge of cluster management and orchestration, Kubernetes