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Salary: USD 234,000 - 286,000 / annual
SambaNova Systems is seeking a Senior Principal AI Solutions Engineer to lead the technical direction of the solutions portfolio while remaining hands-on. This role bridges customer needs and platform capabilities, requiring someone who can prototype agentic applications in days and harden them for production.
You will work across the full modern AI stack: open-weight models (Llama, Qwen, DeepSeek, Gemma), agent frameworks (LangGraph, CrewAI, Claude Agent SDK), self-improving evaluation loops, multimodal and voice pipelines, and front-end applications that knowledge workers can adopt.
Key Responsibilities:
Build agentic and self-improving systems: Design single- and multi-agent systems with planning, tool/function calling, MCP, memory, and long-running workflows. Construct feedback loops that improve agents over time through automated evals, LLM-as-judge, trace-driven optimization (DSPy-style), synthetic data generation, and fine-tuning from production signals. Set up eval harnesses, tracing, and guardrails for quality, cost, and latency measurement.
Get the most from open models: Choose, adapt, and combine open-weight models for customer use cases, including LoRA/PEFT fine-tuning, distillation, and model routing. Benchmark end-to-end solutions and demonstrate how fast inference changes application capabilities. Validate new models on SambaNova's platform and report gaps to Product and Engineering.
Ship full-stack vertical solutions: Build polished web applications (React/Next.js, TypeScript) for non-technical users. Develop domain solutions for financial services, legal, healthcare, public sector, and research (document analysis, research agents, RAG, knowledge assistants, report generation, workflow automation). Build vision-language and document-understanding pipelines. Create real-time voice agents and audio pipelines where low latency is the product.
Lead with customers and shape the platform: Set technical direction for the solutions portfolio, determining which agentic, multimodal, and vertical patterns to invest in. Act as senior technical authority across solutions engineering, reviewing designs and mentoring engineers. Serve as trusted technical advisor to customer CTOs on architecture and enterprise AI scaling. Lead technical discovery, workshops, hackathons, and co-builds with strategic customers. Publish reference architectures, starter kits, and best-practice guides. Develop tooling for SambaStack deployment and management. Feed field learnings into model and platform roadmaps. Represent SambaNova through demos, talks, blogs, and community contributions.
Requirements:
Bachelor's degree or higher in Computer Science, Electrical Engineering, Applied Mathematics, Physics, Statistics, or 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 in production. Proven experience building agentic systems with tool/function calling, multi-agent orchestration, and frameworks like LangGraph, CrewAI, or OpenAI Agents SDK. 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. Excellent customer communication with ability to lead workshops and explain architecture to executives and engineers. Track record of technical leadership: setting architecture direction, mentoring senior engineers, and owning outcomes for strategic accounts.
Preferred: Experience with self-improving systems (prompt/program optimization, RL from feedback, synthetic data pipelines, continual fine-tuning). Real-time voice agent experience (LiveKit, Pipecat, WebRTC) and speech models (ASR/TTS). Multimodal and vision-language models, document-understanding pipelines. Domain experience in knowledge-work verticals. Familiarity with inference frameworks (vLLM, SGLang, TensorRT-LLM) and hardware-aware tuning. MCP, enterprise-scale RAG, agent security and guardrails. Open-source contributions or technical content in AI community.
