Oportun is hiring a
Staff ML Engineer

Summary

Join Oportun's dynamic team and make a difference in the lives of millions by leading the implementation of a cutting-edge ML infrastructure roadmap, providing technical leadership, and collaborating with data scientists to translate intricate model requirements into optimized data pipelines.

Requirements

• Requires 12+ years of related experience with a Bachelor's degree in Computer Science; or a Master's degree with an equivalent combination of education and experience
• Extensive experience orchestrating the development of end-to-end machine learning infrastructure for intricate and large-scale applications
• Proven record of transformative leadership, guiding technical teams to achieve remarkable outcomes and innovation
• Profound mastery of machine learning frameworks such as TensorFlow, PyTorch, or equivalent, coupled with Python programming
• Deep expertise in containerization (Docker) and orchestration (Kubernetes) for orchestrating complex machine learning applications
• Thorough comprehension of software engineering principles, version control (Git), and collaborative development workflows
• Adeptness with cloud platforms (AWS or Azure) and utilization of cloud-native services for crafting robust ML infrastructure
• Track record of successfully integrating DevOps practices, continuous integration, and continuous deployment (CI/CD) pipelines
• Superior problem-solving acumen and ability to navigate intricate technical challenges with dexterity
• Exceptional communication aptitude, capable of fostering effective collaboration across diverse teams and stakeholders

Responsibilities

• Set the strategic vision and lead the implementation of a cutting-edge ML infrastructure roadmap, encompassing all facets from model inception to deployment
• Provide exceptional technical leadership, mentoring, and guidance to a team of machine learning engineers, fostering a culture of continuous learning and innovation
• Collaborate closely with data scientists to translate intricate model requirements into optimized data pipelines, ensuring impeccable data quality, processing, and integration
• Spearhead the establishment of best practices for model versioning, experiment tracking, and model evaluation to ensure transparency and reproducibility
• Architect and execute model deployment strategies, harnessing containerization (Docker) and orchestration (Kubernetes) for exceptional scalability and reliability
• Engineer automated CI/CD pipelines that facilitate seamless model deployment, monitoring, and continuous optimization
• Define and refine performance benchmarks, and optimize models and infrastructure to achieve peak efficiency, scalability, and robustness
• Remain at the forefront of industry trends and emerging technologies, expertly integrating the latest advancements into our ML ecosystem