Machine Learning Engineer, Twelve Labs

• 10+ years of software development experience, including experience in machine learning engineering
• 5+ years of experience in building end-to-end machine learning systems encompassing infrastructure, MLOps, and data management
• You have experience working with engineers at different levels and have coached them in their career development
• 2+ years of experience managing high output engineering teams
• Proficiency in working with video processing and data pipelining
• Experience in establishing and maintaining secure software and system development environments

Desirable

• As the Lead ML Systems Engineer at Twelve Labs, you will lead the ML Engineering team, driving the development of optimal machine learning systems for video foundation (VFM) and language model (VLM) in production
• Your role encompasses the entire spectrum of machine learning engineering, from optimizing and scaling the inference infrastructure, which involves extensive video processing both in the cloud and on-premise, to model deployment and operations, and data infrastructure
• For the first 3 to 6 months, you will be hands on and actively contribute as an individual contributor in our development process
• As the lead, you will set the technical strategies and goals, recruit top talent, and be responsible for your team’s success, ensuring our machine learning systems exceed user expectations in terms of speed, efficiency, and reliability
• Your expertise will be key in overcoming challenges related to processing vast amounts of video data and deploying sophisticated models in production
• Together with your team, you will work to enhance our VFMOps & VLMOps, contributing to a superior user experience that distinguishes Twelve Labs from its competitors
• Your leadership, technical expertise, and commitment to excellence will be critical to our team's success and our users' satisfaction
• Prioritize the team’s work in building and improving our machine learning systems in production for video foundation and language model (VFM & VLM), in collaboration with senior engineers and other stakeholders
• Inference Infrastructure: Construct the most performant, scalable, and reliable inference engine optimized for Twelve Lab’s video foundation and language models
• ML Deployment & Operations (VFMOps / VLMOps): Lead the initiative in serving the model in the most optimized manner, deploying the pipeline, and automating the model training to deployment process
• Data: Oversee the data infrastructure and preparation of high-quality video data for our training runs
• Design processes (e.g. postmortem review, incident response, on-call rotations) that help the team operate effectively
• Coach and develop your reports to decide how they would like to advance in their careers and help them do so
• Run the team’s recruiting efforts through a period of rapid growth

• Recruiter Phone Screen
• Phone Interview
• Technical Screen
• Onsite Interview
• Reference Checks