Together AICustomer Success Engineer (CSE), GPU Cluster
Together AI is hiring a Customer Success Engineer (CSE), GPU Cluster in San Francisco, United States. It pays $260k-$290k a year and Level rates it ; you can apply on Together AI.
Level's read on this job
- Level rates this customer service role Works on AI ●●●○: the daily work is on AI products, without building the model.
- Pay on this posting starts 160% higher than the typical customer service posting on Level.
- The posting mentions Cursor, Elevenlabs and Decagon among its tools and skills.
- Level classes it as a senior role.
- Together AI has 83 other open roles on Level.
AI in this role
Manage large-scale GPU infrastructure deployments and technical customer success for AI and HPC clusters.
About the role
As a Customer Success Engineer at Together AI, you will serve as the named technical owner for one of our most strategic customer relationships. You will be the primary technical point of contact across all infrastructure domains — compute, networking, storage, and facilities — ensuring flawless delivery and operational health of large-scale GPU deployments. This role sits at the intersection of deep infrastructure expertise and high-stakes customer partnership, making you a critical driver of both customer success and company growth.
Responsibilities
- Serve as the named technical point of contact for a dedicated strategic customer, owning the end-to-end technical relationship across compute, networking, storage, and facilities
- Drive structured engagement through regular cadences — status reporting, technical steering meetings, quarterly business reviews (QBRs), and executive business reviews (EBRs) — spanning both operational and strategic levels
- Translate customer operational feedback into actionable input for Engineering, Product, and Infrastructure roadmaps
- Lead issue lifecycle management, escalation, and RCA authorship across all infrastructure domains in partnership with Support, SRE, DC Ops, and Engineering teams
- Own end-to-end RMA coordination and hardware lifecycle management, including acceptance testing, spare inventory management, and hardware health reporting for large-scale GPU deployments
- Maintain deep technical expertise across the customer's infrastructure stack — GPU compute, high-speed fabric, and large-scale storage systems — advising on configuration, operational best practices, and incident resolution
- Own the observability strategy for the customer estate, including alert policy definition, dashboard development, and proactive health management across all infrastructure layers
- Coordinate DC operations and facilities events in partnership with internal teams and hosting providers, ensuring SLA compliance and cluster availability
- Act as project manager for all capacity expansions, owning the full node deployment lifecycle from freight receipt through production acceptance
Qualifications
- 5+ years in a customer-facing technical role, with 2+ years in dedicated technical account management or solutions architecture for large-scale AI or HPC infrastructure
- Deep expertise in GPU infrastructure — GPU health diagnostics, RMA workflows, and hardware acceptance testing
- Hands-on experience with large-scale Ethernet and InfiniBand fabric architecture
- Working knowledge of enterprise storage systems, including high-density NVMe, parallel file systems, and metadata infrastructure
- Experience with DC operations, facilities coordination, and hosting provider SLA management
- Strong ownership mindset for incident management, RCA authorship, and executive-level customer communication
- Proficiency in infrastructure monitoring and observability tooling (Prometheus, Grafana, or equivalent)
- Proven ability to manage multiple concurrent workstreams with hyperscaler-level rigor and communication standards
- Proficiency in Python, Bash, or infrastructure automation tools preferred
About Together AI
Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month.
Compensation
We offer competitive compensation, startup equity, health insurance, and other benefits, as well as flexibility in terms of remote work. The US base salary range for this full-time position is: $260-290K OTE + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.
Location
San Francisco, CA (Hybrid) or New York, NY (Hybrid)
Equal Opportunity
Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.
Please see our Privacy Policy at https://www.together.ai/privacy
How we rate this
Customer Success Engineer (CSE), GPU Cluster at Together AI rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in customer service jobs at this level
Pay
- This job starts at
- $260k
- Typical customer service job
- $100k
This posting starts 160% above the typical customer service job. See customer service jobs
Questions you could be asked
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- Tell me about a project where infrastructure was part of your work. What did you do?
- Tell me about a project where customer success was part of your work. What did you do?
- Tell me about a project where hardware diagnostics was part of your work. What did you do?
- Tell me about a project where hpc was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Fine-tuning, Infrastructure, Customer Success, Hardware Diagnostics, and Hpc. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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