Senior ML Ops Engineer, Sprout Social

• We know experience is built in a number of ways. Even if your background doesn’t match the exact requirements, we encourage you to apply and share any relevant skills in a cover letter
• Sprout welcomes all candidates to apply, including those who identify as BIPOC, women and underrepresented genders in tech, LGBTQIA+, parents, veterans, persons with disabilities and people of all faiths
• We’re looking for a creative, collaborative, pragmatic, highly motivated, and impact oriented technical leader to join our team in building great software
• If you can solve hard problems, deliver quality server-side software, and confidently guide your peers to learn from and teach each other, we’d love to talk with you!
• 5+ years of experience developing and supporting AI/ML software in a production environment
• 5+ years of experience programming in object-oriented languages such as Java, Python, or C++
• Impact-oriented mindset with an interest in stability at scale and a willingness to engage in feature development
• 3+ years of experience building and supporting GPU-heavy services

Desirable

• Build and maintain infrastructure using AWS, Terraform, and Kubernetes to support AI/ML at scale, including Generative AI applications
• Manage the end-to-end lifecycle of machine learning models, ensuring observability and tooling support both scale and speed
• Execute at scale while staying nimble enough to keep up with new capabilities being offered by social network APIs
• Improve processes and champion ideas that matter while holding the team accountable to high code quality and engineering standards
• Support our AI/ML Scientists by developing tooling to streamline model development and deployment
• Within 1 month, you’ll plant your roots, including:
• Complete Sprout’s New Hire training program alongside other new Sprout team members
• Get acclimated to the team's current Mission, Goals, and Objectives along with future product roadmaps
• Become familiar with the team’s existing deployment patterns and the ML Ops tooling ecosystem
• Within 3 months, you’ll start hitting your stride by:
• Decomposing work into small, similarly sized units and working with your squad to prioritize quarterly team goals
• Setting up initial software for model deployment and monitoring of ML models
• Partnering with the Infrastructure team to deploy an existing ML model in Kubernetes
• Acting as the domain owner for new projects and writing necessary design documents
• Within 6 months, you’ll be making a clear impact through:
• Rolling out monitoring and alerting tools to identify problems before they affect users
• Helping deploy new ML models processing hundreds of millions of messages a day
• Identifying technical debt and performance bottlenecks, and executing plans to improve the code
• Collaborating effectively across the organization to ensure big-picture alignment
• Within 12 months, you’ll make this role your own by:
• Becoming the go-to expert of ML Ops at Sprout
• Developing repeatable deployment patterns for data scientists to train, deploy, and evaluate batch, REST, and event-based ML services
• Owning cross-organizational projects and mentoring junior engineers to help them level up technically
• Surprising us! Use your unique ideas and abilities to change your team in beneficial ways