Senior Machine Learning Engineer, QuickBooks Capital
In this role, you'll be part of a vibrant team of data scientists, product developers and machine learning engineers. You'll be expected to help architect, code, optimize, and deploy machine learning models at scale using the latest industry tools and techniques. As the Senior Engineer - you will be responsible for defining and maintaining the ML Data Architecture for Quickbooks Capital's use-cases. You'll also help automate, deliver, monitor, and improve machine learning solutions. Important skills include software development, systems engineering, data wrangling, feature engineering, architecting, and testing.
- Design and build systems which improve machine learning scalability, usability, and performance.
- Work cross functionally with product managers, data scientists, and engineers to understand, implement, refine, and design machine learning and other algorithms.
- 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.
- Effectively communicate results to peers and leaders.
- Explore the state-of-the-art technologies and apply them to deliver customer benefits.
- Interact with a variety of data sources, working closely with peers and partners to refine features from the underlying data and build end-to-end pipelines.
- BS, MS, or PhD degree in Computer Science or a related field, or equivalent practical experience.
- Strong knowledge of
- 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.
- Machine Learning/Data Science languages, tools, and frameworks (e.g., Spark, Scala, Python, R, SQL, SkLearn, NLTK, Numpy, Pandas, TensorFlow, Keras, Java).
- Machine learning techniques (e.g., classification, regression, and clustering) and principles (e.g., training, validation, and testing).
- Data query and data processing tools/systems (e.g., relational, NoSQL, stream processing).
- Distributed computing systems and related technologies (e.g., Spark, Hive).
- Mathematics fundamentals: linear algebra, calculus, probability
- Knowledgeable with Data Science tools and frameworks (i.e. Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark).
- Knowledge of data query and data processing tools (i.e. SQL)
- Experience using deep learning architectures
- Cloud technologies, in particular AWS.
- DevOps concepts (e.g., CICD), Software container technology (e.g., Kubernetes, Docker)
- Experience with designing and developing deep learning architectures
- Deploying highly scalable software for SaaS products
- Keep up with the industry trends and academia on AI, Machine Learning and state-of-the-art experimental systems
- Experience deploying highly scalable software supporting millions or more users
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