Senior Technical Support Engineer
- Role
- Support
- Experience
- Senior
- Company size
- Startup
Open to GB only. Set where you work from to check your eligibility.
Core skills
Required skills
Who we are
At Domino, we build software that helps the largest, AI-driven organizations build and operate advanced data science and AI solutions at scale. Our platform integrates a streamlined model development environment, MLOps capabilities, and novel features for collaboration, reuse, and reproducibility — all of which make data science teams more productive, reduce time to value, and ensure compliance. Our customers — like Johnson & Johnson, GSK, Bristol Myers, UBS, FINRA and the US Navy — are using our software to solve some of the most important challenges in the world, such as developing new medicines, securing our financial markets, or protecting our country. Backed by Sequoia Capital, Coatue Management, NVIDIA, Snowflake and other leading investors, we have been in business for a decade but are still a small team operating with the spirit of a startup. Especially in the world of AI today, we believe that the future is still being invented — and we want to be the ones building it.
What we are building
As a Technical Support Engineer, you're the bridge between our customers and our Engineering organization. You'll own technical support cases end-to-end, triaging issues across Kubernetes infrastructure, ML platform components, authentication, data connectivity, and model deployment, and ensuring every customer gets a clear and timely resolution. You'll also contribute to the knowledge base that helps the whole team scale.
What your impact will be
Own support cases for enterprise customers across all severity levels, from initial triage through resolution, with clear communication and accurate expectations throughout Diagnose and resolve Kubernetes and cloud infrastructure issues: pod failures, resource limits, persistent volumes, RBAC, ingress, and cluster-level diagnostics Troubleshoot ML platform problems including workspace and job failures, environment build errors, model deployment issues, and data connector failures File detailed, actionable bug reports and enhancement requests in Jira and act as the customer's advocate with Product and Engineering Write and review knowledge base articles, how-to guides, and troubleshooting docs, building the reference layer that helps customers and teammates solve problems faster Hand off cases cleanly in a follow-the-sun model across AMER, EMEA, and APAC, ensuring continuity for global enterprise accounts Run live troubleshooting sessions with customers via video call and participate in EMEA weekend on-call rotation per team schedule
What we look for in this role
3 to 5 years in enterprise technical support, solutions engineering, or a similar customer-facing technical role at a SaaS or data/AI platform company Hands-on Kubernetes: pod lifecycle, kubectl, RBAC, namespaces, persistent volumes, and cluster-level troubleshooting Strong Linux and command-line proficiency: log analysis, process management, file system navigation, and shell scripting Familiarity with Python-based ML workflows: Jupyter, package management, model training and serving Experience with cloud platforms (AWS, GCP, or Azure) and containerized application environments Methodical troubleshooter: you form a hypothesis, test it, and adapt when the logs disagree with your theory Clear written communicator: your case updates and KB articles don't require a follow-up to understand Comfortable managing multiple open, time-sensitive cases without losing the thread on any of them Works well asynchronously across time zones in a remote-first, globally distributed team Bachelor's degree in computer science, engineering, or a related technical field (or equivalent experience)
What we value
We strongly believe in the value of growing a diverse team and encourage people of all backgrounds, genders, ethnicities, abilities, and sexual orientations to apply We value a growth mindset. High-performing creative individuals who dig into problems and see the opportunities for success We believe in individuals who seek truth and speak the truth and can be their whole selves at work We value all of you that believe improving is always possible. At Domino, everything is a work in progress – we can do better at everything We emphasize an environment of teaching and learning to equip employees with the tools needed to be successful in their function and the company
#LI-Remote
What you'll do
- Own support cases for enterprise customers across all severity levels, from initial triage through resolution, with clear communication and accurate expectations throughout
- Diagnose and resolve Kubernetes and cloud infrastructure issues: pod failures, resource limits, persistent volumes, RBAC, ingress, and cluster-level diagnostics
- Troubleshoot ML platform problems including workspace and job failures, environment build errors, model deployment issues, and data connector failures
- File detailed, actionable bug reports and enhancement requests in Jira and act as the customer's advocate with Product and Engineering
- Write and review knowledge base articles, how-to guides, and troubleshooting docs, building the reference layer that helps customers and teammates solve problems faster
What they require
- 3 to 5 years in enterprise technical support, solutions engineering, or a similar customer-facing technical role at a SaaS or data/AI platform company
- Hands-on Kubernetes: pod lifecycle, kubectl, RBAC, namespaces, persistent volumes, and cluster-level troubleshooting
- Strong Linux and command-line proficiency: log analysis, process management, file system navigation, and shell scripting
- Familiarity with Python-based ML workflows: Jupyter, package management, model training and serving
- Experience with cloud platforms (AWS, GCP, or Azure) and containerized application environments
Domino builds software that helps large AI-driven organizations build and operate advanced data science and AI solutions at scale, with model development, MLOps, collaboration, reuse, reproducibility, and compliance capabilities.