Machine Learning Engineer
Experimentation is at the core of what you do. The role is to work to turn business questions into data analysis effectively and provide meaningful recommendations. This is a unique hybrid role that will focus on your knowledge of data infrastructure and your ability to drive insights.
- Develop highly scalable systems, al;rithms, and tools on one platform to support machine learning and deep learning solutions.
- Develop, integrate, and optimize end to end AI pipeline.
- Collect, analyze, and synthesize requirements and bottleneck in the technology, systems, and tools used by machine learning engineers and scientists, develop solutions that improve efficiency, leverage more amount of data efficiently.
- Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, GPU, TPU and FPGA)
- Explore state-of-the-art deep learning techniques
- Partner with data science and domain engineering teams to support the business transformation through AI.
- A Bachelor's degree in computer science, data science, mathematics, or a related field.
- 3 – 4 years of proven work experience as a Machine Learning Engineer or similar role
- A good understanding of data structures, data modeling and software architecture
- Great knowledge of math, probability, statistics and algorithms
- A great ability to write robust code in Python, Java and R
- A strong familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)
- Superb analytical and problem-solving abilities.
- Great communication and collaboration skills.
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