Distributed Machine Learning - Platform

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

Your Role:

• Scaling DS model for serving 100M+ users.
• Scaling DS development lifecycle, Enabling model training and inferencing at scale.
• Designing robust, scalable platform for enabling DS personalized model.
• Scaling M/L algorithms, processing huge dataset with distributed processing systems like Apache Spark/Apache Flink.
• Work with the data engineering team to design effective solutions for ETL pipelines and Near real-time data aggregations.
• Leading cross-functional initiatives and collaborating with engineers across teams.

Must Have:

• Industry experience as MLE or related field for at least 3 years
• Experience in building batch & streaming applications on platforms like Apache Spark/Apache Flink/KSQL.
• Experience with structuring machine learning systems for production
• Expert & hands-on experience in designing and debugging distributed solutions with NoSQL/In-memory/data-grid tools like (Cassandra/Apache Ignite/Aerospike/HBase/Apache Ignite/Scylladb/Voltdb)
• Thoroughly versed with distributed technologies, strategies for building low latency, high throughput real-time systems are highly scalable.

Good to Have:

• Experience with feature engineering at scale, i.e. experience with workflow management platforms like airflow/prefect/step/Jenkins or equivalent ones
• Understand Linear Algebra / Numerical Computing as in Gilbert Strang's book
• Experience in applied data analysis, including A/B testing
• Experience with large-scale distributed data processing systems, cloud infrastructure such as AWS, Azure or GCP, and container systems such as Docker
• Completed CS 329S: Machine Learning Systems Design course