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The Team 

Upstart's GenAI Core Platform team builds the foundational Platform that democratizes access to generative AI across the company. As a Principal Engineer - GenAI/LLM, you will provide technical leadership across multiple cross-functional teams, shaping the vision, architecture, and adoption strategy for our new GenAI platform for Engineers and non-Engineers alike. 

You will work closely with engineering leaders, ML researchers, and product stakeholders to ensure Upstart can scale the safe, efficient, and responsible use of LLMs across all business domains.

This role goes beyond delivery. It is about setting long-term direction, de-risking our most ambitious AI initiatives, and raising the bar for technical excellence across the organization. In short, you will have a direct influence and impact on how we enable and implement GenAI and LLMs internally at Upstart.

How You'll Make an Impact

• Define and drive the multi-year technical roadmap for the Core GenAI Platform, influencing adoption and alignment across several engineering teams
• Collaborate with CTO/VP-level and above leadership to influence business strategy with technical insight
  Lead architecture and design of large-scale, mission-critical systems for model inference, orchestration, and compliance
  Identify and solve systemic technical risks that impact the entire company and business-critical AI initiatives
• Represent Upstart internally and externally as a thought leader in GenAI systems through executive reviews, cross-company forums, and industry venues
  Establish operational standards for GenAI integrations, ensuring security, fairness, and reliability at scale
• Build high-leverage, generalizable platform components that meaningfully accelerate the productivity of engineers across Upstart
• Mentor senior engineers, other principal engineers, and shape the growth of future technical leaders, fostering a culture of learning and excellence

Minimum Qualifications

• Bachelor's degree in Computer Science, Engineering, or Mathematics, or a related field (or its equivalent) + 8 years of experience
• Deep expertise in LLM-specific infrastructure such as inference optimization (quantization, ONNX, streaming).
• Ability to design scalable RAG architectures and manage the lifecycle of models from experimentation to high-availability production environments including vector databases. 
• Proven experience building robust, automated evaluation pipelines using both heuristic metrics and 'LLM-as-a-judge' frameworks to ensure model accuracy and safety.
• Hands-on experience with LLM toolchains such as LangChain, LlamaIndex, or OpenAI APIs
• Experience designing and operationalizing ML/LLM platforms at scale, including optimization for cost, latency, and reliability and  the platform infrastructure needed to support them.
• Deep Expertise in best practices and risks for GenAI including hallucination mitigation, fairness, explainability, and data privacy

Preferred Qualifications

• 12+ years of experience and 5+ leading cross functional technical initiatives
• Proven ability to execute or guide model fine-tuning and alignment (RLHF/DPO) to optimize LLM performance for specialized consumer use cases and proprietary datasets.
• Full-stack engineering experience (Python, FastAPI, Kotlin, Spring, React/TypeScript, or similar)
• Deep expertise in backend systems and infrastructure technologies such as Kubernetes, Docker, Terraform, and cloud-native architectures

Position location This role is available in the following locations: Remote USA/Canada with quarterly travel.

Time zone requirements The team operates on the East/West coast time zones. 

Travel requirements As a digital first company, the majority of your work can be accomplished remotely. The majority of our employees can live and work anywhere in the U.S but are encouraged to to still spend high quality time in-person collaborating via regular onsites. The in-person sessions' cadence varies depending on the team and role; most teams meet once or twice per quarter for 2-4 consecutive days at a time.

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