Engineering Manager, ML Platform

We are looking for an Engineering Manager to define a new function in R&D at Synthesia. We are creating an ML Platform to accelerate our research teams and we want you to define how that should be done. You will be working directly with the CTO and supporting 7+ teams that work "full stack". Our Research Engineers run Ops, Engineering and Science. We have built our own workflow engine and data pipelines on AWS. Now we want to streamline the process, adopt the right tools and super-charge the work we are doing. 🔬You are someone that focuses on the developer experience, you have a close attention to detail and you create and communicate clear, well-defined processes. You love to support and help others. The happiest day is when you hear "it was just 1-click and everything worked". You love to build systems that unblock others and unlock scale. You are used to working in a fast-paced start-up environment. 👩‍💼 You will join a group of more than 30 Researchers and Engineers in the R&D department. This is an open, collaborative and highly supportive environment. We are all working together to build something big - the future of synthetic media and programmable video through Generative AI. We are proud of the culture, as well as the impact of the technology we are building.   What will you be doing? 🚀 In this position, you will define the roadmap for both ML Ops and Data Ops in R&D at Synthesia. You will manage a team of 3-5 people in the first instance and you will be hands-on implementing the roadmap as well as setting out the vision. We want you to: define the setup for ML model development and deployment to accelerate our research teams define our “1 click process” to create custom services and data-pipelines set up our compute platform on AWS for model training, management and deployment set up our data pipelines and data management, transforms, versioning and tracking build our ML Ops function to support a group of 40 in R&D with 9 ML teams build our Data Ops function to support data at scale with large model training   Who are you? We are looking for candidates that can own the roadmap to build our ML Platform function. You will have: 6+ years experience in Engineering / ML Ops / Data Ops / Data Science. Setup ML development and deployment at scale before. You know what works and what doesn't work. In depth experience working with AWS for data and compute. You are happy working side-by-side with DevOps to define our infra. Experience defining ML Ops for model training, management, deployment, serving, logging. Strong understanding of Data Ops with dataset management, versioning, usage tracking, logging. Defined the setup for ML experimentation and data pipeline orchestration. Experience managing a small team with high impact outcomes. Experience supporting deep tech teams working with PyTorch and containerized development with Docker. Outstanding communication skills.   Nice to have… If you have seen large scale model training with 1000s models built a day through data pipelines with 100s component services. If you have seen multi-GPU large model training using large scale audio-video datasets with 10000s of hours of content. If you have created a streamlined platform to support world class research teams spanning tech planned direct to product, to foundational research for top-tier academic conferences. We would love to talk to you. We'd also love to talk to you - if this what you dream of doing. 😎    The good stuff... 💸 You will be compensated well (salary + stock options + bonus) 📍 You will work in a hybrid setting with an office in London 🏝 You get 25 days of annual leave + public holidays 🥳 You will join an established company culture with regular socials and company retreats 🤩 You get 4 weeks paid sabbatical after 4 years at the company + $10,000!! 🍼 You get a paid parental leave 👉 You can participate in a generous referral scheme 💻 You get a brand new computer of your choice (if that still counts as a benefit in 2023 🤔) 🚀 You will have huge opportunities for your career growth