Senior Principal AI Solutions Engineer

Join SambaNova, a leading AI solutions company, as a Senior Principal AI Solutions Engineer. In this role, you will set the technical direction for our solutions portfolio, engage with strategic customers, and mentor the wider solutions team. You will work across the entire modern AI stack, build agentic and self-improving systems, and develop polished web applications for non-technical knowledge workers. This position offers flexible PTO, parental leave, and various benefits.

• Lead the design and development of agentic and self-improving systems, ensuring they are reliable enough for production.
• Set the technical direction for the solutions portfolio, making decisions on which agentic, multimodal, and vertical patterns to invest in.
• Act as the senior technical authority across solutions engineering, mentoring engineers and lifting the quality of everything the team ships.

• Excellent customer communication, with the ability to lead workshops and explain architecture to both executives and engineers

A track record of technical leadership across teams: setting architecture direction, mentoring senior engineers and owning outcomes for strategic accounts

• Bachelor's degree or higher in Computer Science, Electrical Engineering, Applied Mathematics, Physics, Statistics or a related field

8+ years (IC5) or 10+ years (IC6) of industry experience in software, ML or solutions engineering, including 3+ years building LLM-based applications that reached production
Proven experience building agentic systems: tool and function calling, multi-agent orchestration and frameworks such as LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK or equivalent

• Hands-on experience with open-weight models: serving, prompting, fine-tuning (LoRA/PEFT) and evaluation
• Strong full-stack skills: expert Python plus modern front-end development (TypeScript, React/Next.js), APIs and cloud deployment

Experience designing evals and benchmarks for LLM applications, covering quality, latency and cost

• Experience building self-improving or learning systems: prompt/program optimization, RL from feedback, synthetic data pipelines or continual fine-tuning

Experience building real-time voice agents (e.g. LiveKit, Pipecat, WebRTC) and working with speech models (ASR/TTS)
Experience with multimodal and vision-language models and document-understanding pipelines

• Domain experience in one or more knowledge-work verticals (financial services, legal, healthcare, public sector, scientific research)

Familiarity with inference frameworks (vLLM, SGLang, TensorRT-LLM) and hardware-aware performance tuning
Experience with MCP, RAG at enterprise scale, and agent security and guardrails

• Open-source contributions, public demos or technical content in the AI community