Machine Learning Engineer - McLean/Richmond (Hybrid and W2 Only)

Machine Learning Engineer

Duration: 7 Months + Possible Extension

Location: McLean, VA or Richmond, VA Would need to be hybrid onsite 3 times per week.

Interview: 45 minutes zoom video interviews w/ Victor and/another team leads

Must Have:

• 4 years of Python, Scala, or Java
• 3 years of experience designing and building data-intensive solutions using distributed computing
• 2 years of on-the-job experience with an industry recognized ML frameworks (scikit-learn, PyTorch, Dask, Spark, or TensorFlow) preferably Spark
• 1 year of experience productionizing, monitoring, and maintaining models
• AWS
• Snowflake (or some other database knowledge is acceptable)

Job Description:

• Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams.
• Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation).
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
• Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.
• Construct optimized data pipelines to feed ML models.
• Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
• Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.
• Use programming languages like Python, Scala, or Java

Job Requirements:

• 1+ years of experience building, scaling, and optimizing ML systems
• 1+ years of experience with data gathering and preparation for ML models
• 2+ years of experience developing performant, resilient, and
• 2+ maintainable code
• Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field
• 3+ years of experience with distributed file systems or multi-node
• 3+ database paradigms
• Contributed to open-source ML software
• Authored/co-authored a paper on a ML technique, model, or proof of concept
• 3+ years of experience building production-ready data pipelines that feed ML models
• Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance