Description
We are looking for a Principal ML Solutions Architect to act as the most senior technical authority for customers leveraging Token Factory's serverless inference and fine-tuning platforms.
The successful candidate will design and implement optimized inference and fine-tuning workflows, set technical direction across our largest and most strategic accounts, own the hardest performance and quality problems end to end, mentor other Solutions Architects, and serve as a primary technical voice shaping the platform roadmap with backend, product, and research teams.
Responsibilities:
- Own the most complex, highest-stakes customer engagements from architecture through production across multiple modalities, driving measurable business value
- Optimize LLM inference at the framework and hardware level and codify the resulting best practices into reusable playbooks for the team
- Lead supervised and reinforcement fine-tuning efforts to maximize model quality
- Design and implement production-ready LLM solutions using Token Factory's inference services
- Provide deep technical expertise in prompt engineering, RAG architectures, model selection, and cost/performance trade-offs at scale
- Partner closely with product, engineering and research to surface customer needs, prototype platform features, and directly influence the roadmap
- Guide customers from PoC to production with a focus on performance, reliability, and cost efficiency , and define the standards by which the team does so
- Mentor Senior and mid-level Solutions Architects; raise the technical bar of the team through review, enablement, and knowledge sharing
- Represent Token Factory externally through talks, blog posts, and conferences
Requirements:
- 8+ years of experience in ML/AI systems, with at least 4 years focused on LLMs and generative AI
- Demonstrated technical leadership: owning ambiguous, high-impact problems end to end and influencing decisions across teams and customers
- Expert knowledge of the LLM ecosystem: model architectures, fine-tuning approaches, and inference internals
- Deep, hands-on command of inference optimization: quantization, KV-cache management, batching, routing, etc.
- Hands-on experience with:
- Running LLMs in production at scale: deploying, operating, and debugging inference workloads down to the framework level
- LLM fine-tuning, including SFT/LoRA and data preparation/curation; experience with RL-based fine-tuning
- LLM evaluation: building task-specific benchmarks and offline/online eval pipelines, including LLM-as-a-judge setups
- Inference frameworks and libraries (vLLM, SGLang, TensorRT-LLM), including the ability to read, modify, and contribute to their internals
- Deploying LLM-powered applications using APIs from OpenAI, Anthropic, or open-source models
- Strong Python programming skills
- Excellent communication skills, with the ability to clearly explain technical concepts to diverse audiences, from engineers to executives
Benefits:
- Health Insurance: 100% company-paid medical, dental, and vision coverage for employees and families
- 401(k) Plan: Up to 4% company match with immediate vesting
- Parental Leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers
- Remote Work Reimbursement: Up to $85/month for mobile and internet
- Disability & Life Insurance: Company-paid short-term, long-term, and life insurance coverage