Machine Learning Engineer 5

About the job

Responsibilities

• Design, build, and deliver ML models and components to solve business problems in collaboration with Product and Data Science teams.
• Build and scale multi-tenant platforms for large footprint ML model training and/or serving.
• Inform ML infrastructure decisions based on understanding of ML modeling techniques, including model choice, data and feature selection, training, hyperparameter tuning, and bias/variance.
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
• Collaborate within a cross-functional Agile team to create and enhance software for big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage or build cloud-based architectures and platforms for optimized ML model delivery at scale.
• Construct optimized data pipelines to feed ML models.
• Apply continuous integration and continuous deployment best practices, including test automation and monitoring, for successful ML model and application code deployment.
• Ensure code is well-managed for reduced vulnerabilities, models are governed for risk, and ML practices follow Responsible and Explainable AI guidelines.
• Use programming languages such as Python, Scala, or Java.

Requirements

• Bachelor's Degree or higher in Computer Science, Machine Learning, or a related quantitative field.
• At least 6 years of experience programming with Python, Java, Golang, or C++.
• At least 6 years of Machine Learning experience using industry standard frameworks like PyTorch or Tensorflow and libraries such as Pandas, NumPy, Scikit-learn.
• At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) for AI or Machine Learning data preparation.
• At least 4 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure).
• At least 4 years of experience using Kubernetes to manage large scale containerized Machine Learning software systems.
• Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field (preferred).
• 5+ years of experience optimizing ML algorithms, configurations, and infrastructure (preferred).
• 5+ years of experience following software development best practices including source control, testing, code reviews, CI/CD (preferred).
• 5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response plans (preferred).
• 5+ years of experience with Machine Learning techniques (Supervised, semi-supervised, unsupervised, reinforcement learning), model types (Regression, Classification, Clustering), architectures (RNNs, CNNs, LSTMs, Transformers), training concepts, and model evaluation (preferred).
• 5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models (preferred).
• Demonstrated ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents (preferred).
• Ability to communicate complex technical and machine learning concepts clearly to various audiences (preferred).

Benefits

• Full-time annual salaries ranging from $229,900 to $262,400 for McLean, VA location.