Participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms
Focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications
Perform many ML engineering activities, including 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 ML infrastructure decisions using 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

Requirements

Bachelor’s Degree
At least 4 years of experience programming with Python, Scala, or Java (Internship experience does not apply)
At least 3 years of experience designing and building data-intensive solutions using distributed computing
At least 2 years of on-the-job experience with an industry recognized ML frameworks (scikit-learn, PyTorch, Dask, Spark, or TensorFlow)
At least 1 year of experience productionizing, monitoring, and maintaining models
1+ years of experience building, scaling, and optimizing ML systems (Preferred)
1+ years of experience with data gathering and preparation for ML models (Preferred)
2+ years of experience developing performant, resilient, and maintainable code (Preferred)
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform (Preferred)
Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field (Preferred)
3+ years of experience with distributed file systems or multi-node database paradigms (Preferred)
Contributed to open source ML software (Preferred)
Authored/co-authored a paper on a ML technique, model, or proof of concept (Preferred)
3+ years of experience building production-ready data pipelines that feed ML models (Preferred)
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance (Preferred)

Benefits

Performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
Comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonScalaJavamachine learningML frameworksscikit-learnPyTorchDaskSparkTensorFlow

Soft Skills

collaborationproblem-solvingcommunicationAgile methodologyteamworkcritical thinkingadaptabilityattention to detailcreativitytime management