Remote Staff Software Systems Engineer, Machine Learning

Summary

Join us as a Staff Software Systems Engineer-Machine Learning Platform (Development)-Python, Spark, PySpark and help drive innovative vision for various products and platforms. Lead end-to-end software development lifecycle, craft high-performance machine learning code, and mentor other engineers.

Requirements

• Degree in mathematics/computer science or related discipline
• 5+ years of experience in the complete software development lifecycle including design, coding, code reviews, testing, build processes, deployments and operations
• 5+ years of experience in programming, with proficiency in at least one programming language, preferably Python or Java
• 3+ years of experience in leading the design and architecture of large distributed systems preferably on cloud platforms (e.g., AWS, Azure, Google Cloud)
• Experience working with distributed data and ML technologies (e.g. MapReduce, Spark, Flink, Kafka, PySpark, SageMaker etc.)
• Experience as a mentor, tech lead or leading an engineering team
• Adept at tackling highly complex, ambiguous or undefined problems

Responsibilities

• Be a thought leader and forward thinker, help drive an innovative vision for our various products and platforms
• Take the lead in the end-to-end software development lifecycle, encompassing design, testing, deployment, and operations
• Craft high-performance, production-ready machine learning code for our next-generation real-time ML platform
• Working closely with other engineers and scientists, lead solutions to accelerate model development, validation and experimentation cycles
• Mentor and develop other engineers on the team, establish technical direction and foster team culture
• Uphold the highest standards of technical rigor in engineering and operational excellence, build highly resilient and scalable systems

Preferred Qualifications

• M S or PhD in Computer Science or equivalent experience in ML
• Experience dealing with real-world large-scale datasets
• Prior experience delivering end-to-end ML solutions, including data preparation, training, fine-tuning and deployment of large models
• Prior experience in developing ML optimization techniques in frameworks like PyTorch and CUDA