Software Engineer, Machine Learning Responsibilities

Collaborate with cross-functional teams (product, design, operations, infrastructure) to build innovative application experiences
Implement custom user interfaces using latest programming techniques and technologies
Develop reusable software components for interfacing with back-end platforms
Analyze and optimize code for quality, efficiency, and performance
Lead complex technical or product efforts and provide technical guidance to peers
Architect efficient and scalable systems that drive complex applications
Identify and resolve performance and scalability issues
Work on a variety of coding languages and technologies
Establish ownership of components, features, or systems with expert end-to-end understanding

Minimum Qualifications

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
Track record of setting technical direction for a team, driving consensus and successful cross-functional partnerships
6+ years of programming experience in a relevant language or 3+ years of experience + PhD
Experience building maintainable and testable code bases, including API design and unit testing techniques

Preferred Qualifications

Experience building and shipping high quality work and achieving high reliability
Experience improving quality through thoughtful code reviews, appropriate testing, proper rollout, monitoring, and proactive changes
Experience with developing machine learning models at scale from inception to business impact
Exposure to architectural patterns of large scale software applications
Experience with scripting languages such as PyTorch, TensorFlow, Python, JavaScript or Hack
2+ years of experience in one or more of the following areas: machine learning, recommendation systems, pattern recognition, data mining, artificial intelligence, or a related technical field
Knowledge developing and debugging in C/C++ and Java
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

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