Manager, ML Solutions Architecture (Token Factory)

Location: Remote - United States

Salary: $228,000–$285,000 USD per year

Company

Builds a full-stack AI cloud platform for data and model training, inference, and production deployment, including the Token Factory serverless platform for open-source LLM inference and fine-tuning.

What you will do

• Manage and grow a regional team of Solutions Architects, including goal setting, performance reviews, promotion cases, onboarding, and individual development plans.
• Own PoC delivery and post-sales technical support outcomes, including optimization timelines, success-criteria attainment, and production handoffs.
• Review benchmarking methods, serving configurations, results, and closure documentation before customer delivery.
• Coordinate staffing, escalations, account coverage, and delivery across teams and time zones.
• Maintain documentation, runbooks, onboarding materials, definitions of done, closure templates, and ticket-tracking standards.
• Translate recurring customer issues into platform priorities and provide leadership with clear assessments of feasibility, account health, and capacity.

Requirements

• 3+ years of experience managing technical teams, including performance management and difficult conversations.
• Experience managing a customer-facing team working on customer timelines and escalations.
• Strong knowledge of LLM architectures, fine-tuning, evaluation design, inference internals, quantization, KV-cache management, batching, routing, and speculative decoding.
• Experience with or working knowledge of Python, vLLM, SGLang, TensorRT-LLM, and the ability to review benchmark methodology and code.
• Excellent communication skills for technical and executive audiences, including enterprise customers.
• Comfort with documentation, process design, reporting, ambiguity, and distributed teams.

Nice to have

• Experience scaling a team from 5 to 15+ while maintaining delivery quality.
• Customer-facing technical experience at a cloud, inference, or AI infrastructure provider.
• Hands-on experience running LLMs in production and debugging inference workloads.
• Experience with multimodal AI models, Docker, Kubernetes, and infrastructure-as-code.

Culture & Benefits

• Competitive compensation and benefits.
• Career growth and learning opportunities.
• Flexibility, ownership, and an international working environment.
• Opportunity to work on impactful AI projects with distributed teams.
• Applicants must be authorized to work in the country in which they apply and provide proof of employment eligibility.