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Key Responsibilities:Develop high-performance, production-ready code optimized for large-scale, high-traffic applications.
Analyze vast datasets to uncover actionable insights, design scalable algorithms, and seize opportunities for innovation.
Key job responsibilities
Collaborate closely with Software Development Engineers to seamlessly integrate machine learning models into production systems, ensuring they meet the stringent latency requirements necessary to process millions of events per day. Address engineering challenges by developing innovative workarounds and optimizations, enabling the efficient deployment and scalability of these models in real-world environments.A day in the life
8:30 AM – Start the Day with Focus10:00 AM – Deep Dive into Research
12:00 PM – Lunch and Networking
1:00 PM – Model Development and Experimentation
3:30 PM – Collaboration Time
Meet with engineers to optimize deployment strategies for models in production. Discussions often focus on meeting latency requirements for real-time inference without compromising accuracy. This synergy between science and engineering is key to delivering scalable solutions.4:30 PM – Results Review and Insights
Analyze experimental results, document findings, and prepare for upcoming iterations. Insights gained here shape the next steps, whether it’s tweaking hyperparameters or exploring a new approach entirely.5:30 PM – Wrap-Up
6:00 PM – Personal Development and Learning
- PhD, or a Master's degree and experience in CS, CE, ML or related field
- Experience in building models for business application
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience using Unix/Linux
- Experience in professional software development
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