Senior Principal AI Solutions Engineer

SambaNova Systems; San Jose (CA); On-site; USD 260,000 - 380,000; Full time

Benefits offered by this job

Flexible PTO in US

Parental Leave

Medical, dental and vision benefits

Flexible Spending Accounts

401k/Pensions

Gym Access

Flexible Working Hours

Job summary

SambaNova Systems is seeking a Senior Principal AI Solutions Engineer to lead hands-on engagements, set the technical direction, and build production-grade agentic AI systems across a broad modern AI stack. You will mentor the solutions team, shape platform capabilities, and work with customers to turn problems into deployable, scalable solutions.

Responsibilities span designing multi-agent architectures, self-improving loops, and end-to-end deployments with a strong emphasis on production

Qualifications

• Bachelor’s degree or higher in Computer Science, Electrical Engineering, Applied Mathematics, Physics, Statistics or related field.
• 8+ years 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 or equivalent.
• Hands-on experience with open-weight models: serving, prompting, fine-tuning (LoRA/PEFT) and evaluation.
• Strong full-stack skills: Python plus front-end (TypeScript, React/Next.js) and APIs.
• Experience designing evals and benchmarks for LLM applications, covering quality, latency and cost.
• Experience with multimodal and vision-language pipelines and real-time voice agents is a plus.
• Ability to mentor engineers and engage with CTOs and senior executives.

Responsibilities

• Lead hands-on engagements with our most strategic customers and mentor the wider solutions team.
• Set the technical direction for the solutions portfolio and feed learnings back into the platform.
• Build agentic and self-improving systems with automated evals and feedback loops.
• Architect reference architectures, starter kits and open-source examples for scalable deployment.
• Develop polished, usable front-end applications (React/Next.js) for knowledge workers.
• Engage with customer CTOs on architecture, build-vs-buy, and enterprise scaling.

Skills

Hands-on technical leadership

Solution architecture

Customer engagements

Mentorship

Python

TypeScript

React/Next.js

Cloud deployment

Eval benchmarks

Self-improving systems

Agentic systems

Open-weight models

Education

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

Tools

LangGraph

CrewAI

OpenAI Agents SDK

Claude Agent SDK

Job description

• We are looking for a Senior Principal AI Solutions Engineer: a hands-on technical leader who sets the technical direction for our solutions portfolio and still builds
• This is someone who can go from a customer problem to a polished, working, agentic application in days, and then harden it for production
• You will work across the whole modern AI stack: open-weight models, agent frameworks and tool use, self-improving evaluation loops, multimodal and voice pipelines, and the front end that makes it all usable by knowledge workers
• You will lead hands-on engagements with our most strategic customers, mentor and raise the bar for the wider solutions team, and feed what you learn back into the platform
• Build agentic and self-improving systems

Agentic AI Architecture: Design and build single- and multi-agent systems with planning, tool and function calling, MCP, memory, and long-running workflows that are reliable enough for production

• Self-Improving Systems: Build feedback loops that make agents better over time, including automated evals, LLM-as-judge, trace-driven prompt and program optimization (e.g. DSPy-style), synthetic data generation, and fine-tuning from production signals
  Evaluation and Observability: Set up eval harnesses, tracing and guardrails so customers can measure quality, cost and latency, and trust what they deploy
• Open-Weight Model Expertise: Choose, adapt and combine open models (e.g. Llama, Qwen, DeepSeek, gpt-oss, Gemma, GLM) for customer use cases, including LoRA/PEFT fine-tuning, distillation and model routing
• Performance and Benchmarking: Benchmark end-to-end solutions, not just tokens per second, and show where fast inference changes what an application can do
• Model Integration: Validate new models and capabilities on SambaNova’s platform stack and report gaps to Product and Engineering
• Front-End and Product Build: Build polished, usable web applications (e.g. React/Next.js, TypeScript) that non-technical knowledge workers can adopt, not just notebooks and APIs
• Vertical Knowledge Work: Build domain solutions for areas such as financial services, legal, healthcare, public sector and research: document analysis, research agents, RAG and knowledge assistants, report generation and workflow automation
• Multimodal Solutions: Build vision-language and document-understanding pipelines (OCR, charts, forms, images, video) combined with agentic reasoning
• Audio and Voice: Build real-time voice agents and audio pipelines (ASR, TTS, speech-to-speech, streaming and turn-taking) where low latency is the product
• Solutions Technical Strategy: Set the technical direction for the solutions portfolio: which agentic, multimodal and vertical patterns we invest in, the shared frameworks and components we build, and the engineering standards they meet
• Technical Leadership and Mentorship: Act as the senior technical authority across solutions engineering; review designs, mentor engineers and lift the quality of everything the team ships
• Executive Engagement: Act as the trusted technical advisor to customer CTOs and AI leaders on architecture, build-versus-buy and scaling AI across the enterprise
• Customer Engagement: Lead technical discovery, workshops, hackathons and hands-on co-builds with strategic customers, from prototype to production
• Reference Architectures: Publish reference architectures, starter kits, open-source examples and best-practice guides that scale beyond each engagement
• SambaStack Tooling: Develop tooling and automation for SambaStack deployment, management and integration
• Product Feedback: Feed what you learn in the field (model requests, feature gaps, developer experience) into the model roadmap and platform priorities
• Thought Leadership: Represent SambaNova through demos, talks, blogs and community contributions

Benefits

• Flexible PTO in US
• Parental Leave
• Benefits (medical, dental and vision)
• Flexible Spending Accounts
• 401k/Pensions
• Gym Access
• Flexible Working Hours

Excellent customer communication, with the ability to lead workshops and explain architecture to both executives and engineersA track record of technical leadership across teams: setting architecture direction, mentoring senior engineers and owning outcomes for strategic accountsBachelor’s degree or higher in Computer Science, Electrical Engineering, Applied Mathematics, Physics, Statistics or a related field8+ years (IC5) or 10+ years (IC6) of industry experience in software, ML or solutions engineering, including 3+ years building LLM-based applications that reached productionProven experience building agentic systems: tool and function calling, multi-agent orchestration and frameworks such as LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK or equivalentHands-on experience with open-weight models: serving, prompting, fine-tuning (LoRA/PEFT) and evaluationStrong full-stack skills: expert Python plus modern front-end development (TypeScript, React/Next.js), APIs and cloud deploymentExperience designing evals and benchmarks for LLM applications, covering quality, latency and costExperience building self-improving or learning systems: prompt/program optimization, RL from feedback, synthetic data pipelines or continual fine-tuningExperience 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 pipelinesDomain 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 tuningExperience with MCP, RAG at enterprise scale, and agent security and guardrailsOpen-source contributions, public demos or technical content in the AI community