Staff Machine Learning Engineer
In this role, you'll be embedded inside a vibrant team of data scientists in order to
bring big data expertise and engineering rigor to our ML solutions. You'll be
expected to help conceive, code, and deploy data science models at scale using the
latest industry tools. Important skills include distributed systems, system design
and architecture, data wrangling, feature engineering, model training and
deployment pipelines, testing metrics.
- Discover data sources, get access to them, import them, clean them up, and make them "machine learning ready".
- Work with data scientists to create and refine features from the underlying data and build pipelines to train and deploy models.
- Partner with data scientists to understand, implement, refine and design machine learning and other algorithms.
- Run regular A/B tests, gather data, perform statistical analysis, draw conclusions on the impact of your models.
- Work cross functionally with product managers, data scientists and product engineers, and communicate results to peers and leaders.
- Explore new technology shifts in order to determine how they might connect with the customer benefits we wish to deliver.
- BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience.
- Knowledge of Big Data tools and frameworks (i.e. Spark, Scala, Cassandra, Hive, SQL).
- Programming experience in Scala, Java/ Python.
- Knowledge of data query and data processing tools (i.e. SQL)
- Computer science fundamentals: data structures, algorithms, performance complexity, and implications of computer architecture on software performance (e.g., I/O and memory tuning).
- Software engineering fundamentals: version control systems (i.e. Git, Github) and workflows, and ability to write production-ready code.
- Experience with integrating applications and platforms with cloud technologies (i.e. AWS and GCP)
- Knowledgeable with Data Science tools and frameworks (i.e. Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark) and/ or interest in learning them.
- Basic knowledge of machine learning techniques (i.e. classification, regression, and clustering) and principles (training, validation, etc.)
- Mathematics fundamentals: linear algebra, calculus, probability
Preferred Additional Qualifications:
- Experience using deep learning architectures
- Experience deploying highly scalable software supporting millions or more users
- Experience with GPU acceleration (i.e. CUDA and cuDNN)
- Interest in reading academic papers and trying to implement state-of-the-art experimental systems
Back to top