Lead Software Engineer - Machine Learning

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

Chick-fil-A is using machine learning and advanced modeling to solve many business-critical problems. In this role you will drive the design and orchestration for a portfolio of over 40,000 models used for new store placement and effects on existing stores. You will be responsible for architecting, designing, and leading the training, model serving, observability, and experimentation of the models the data scientists develop. This effort will leverage the machine learning operations platform maintained by Chick-fil-A which uses an evolving set of machine learning and data tooling and technology. Your daily work will require partnering with fellow engineers, the product owner, data and enterprise architects, data scientists, business stakeholders, vendor teams, and other parties following an agile methodology, while being part of a diverse team that values high performance and excellence as much as work-life balance.

Responsibilities

• Lead, mentor and assess partner engineering teams with minimal supervision
• Own the architecture and design of the required software components for this portfolio of models using Chick-fil-A's machine learning and data ecosystem
• Lead software delivery and operation by serving as a developer and reviewer of code in the following programming languages: Python, PySpark, and R
• Exercise skills in Cloud infrastructure and deployment as well as areas like application security, data analytics, machine learning, and site reliability engineering (SRE)
• Identify & propose emerging technologies, methodologies and/or approaches related to data, analytics, and machine learning operations
• Be a key participant of the team’s Agile process
• Address engineering assignments by autonomously deciding which ones to delegate and which ones to execute hands-on
• Actively monitors relevant KPIs for productionized models

Note - Working in a DevOps model, this opportunity includes both building and running solutions that could require off hours support. This support is shared amongst the team members to cover weekends and weeknights. The goal is to design for failure and, using cloud-native infrastructure patterns, automate responses to issues so they can be worked during normal hours.

Location: Hybrid

Minimum Qualifications

• Bachelor’s Degree or the equivalent combination of education, training and experience from which comparable skills can be acquired
• Broad and deep programming experience in Python
• General understanding of machine learning training and serving
• Experience with source-control systems like Git or Subversion, and CI/CD tools like GitHub Actions or Jenkins
• Experience implementing application security, software design patterns, and the Software Data Lifecycle Configuration
• Good interpersonal and team collaboration skills

Preferred Qualifications

• Master’s Degree or the equivalent combination of education, training and experience from which comparable skills can be acquired
• 5 or more year’s experience
• Experience architecting software solutions on Amazon Web Services (AWS) or other major Cloud Service Provider
• Experience with R programming language
• Experience training, serving, and monitoring machine learning models
• Experience working with an Agile development methodology featuring sprints, point-estimation, and daily standups
• Proficiency in Spark programming or equivalent big data technology
• Experience with Unix/Linux and container technologies such as Docker
• Experience with Airflow, ML Flow, Metaflow, Kubeflow, or similar machine learning orchestration technology

Minimum Years of Experience

3

Travel Requirements

10%

Required Level of Education

Bachelor's degree or equivalent experience

Preferred Level of Education

Masters Degree

Major/Concentration

Computer Science, Analytics Engineering, or related technical field