Summary
Join our team as a Staff Software Systems Engineer-Machine Learning Platform (Development) -Python, Spark, PySpark. We're looking for an experienced engineer to lead the design and launch of strategic machine learning solutions, drive business-wide innovation, and mentor other engineers on the team.
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