About the job
SambaNova is hiring a Forward Deployed Engineer (FDE) for our SambaStack based product portfolio in our Customer Success Organization.
Responsibilities
Embed directly with strategic enterprise customers to design, build, and deploy production GenAI applications on SambaNova's SN40L platform and SambaStack based product portfolio
Architect and implement LLM-powered workflows — including RAG pipelines, multi-agent systems, fine-tuning workflows, and coding solutions — tailored to each customer's data, infrastructure, and business goals.
Optimize AI inference performance on SambaNova hardware; benchmark model throughput, latency, and accuracy against customer requirements and competitor baselines.
Troubleshoot and resolve production issues end-to-end across model, software, and hardware layers — acting as the first and last line of technical escalation in the field.
Translate customer needs into clear product requirements and engineering feedback; serve as the primary voice of field reality to SambaNova's Product and Engineering teams.
Partner with Account Executives and Solutions Engineers to shape technical sales strategy, scope engagements, and demonstrate platform differentiation during evaluations and proof-of-concepts.
Develop reusable accelerators, reference architectures, and internal playbooks that scale learnings from one deployment to many.
Present technical findings, architecture decisions, and roadmap input at customer executive briefings and internal forums; represent SambaNova at industry conferences and events.
Qualifications
Minimum
5+ years of hands-on engineering experience, with a strong record of shipping production AI/ML systems.
Deep expertise in GenAI application development: LLM orchestration, RAG, agentic frameworks (LangChain, LlamaIndex, DSPy), prompt engineering, and evaluation pipelines.
Strong foundations in ML fundamentals — model training, fine-tuning, inference optimization, quantization, and performance benchmarking.
Proficiency in Python (required); working knowledge of C++ or CUDA a strong plus for hardware-layer debugging.
Experience deploying AI workloads on cloud infrastructure (AWS, Azure, GCP) and familiarity with containerization, orchestration (Kubernetes, Docker), and MLOps tooling.
Comfortable engaging directly with customers: able to run technical discovery, set expectations, push back constructively, and present to executive and practitioner audiences alike.
Bachelor's or graduate degree in Computer Science, Electrical Engineering, Mathematics, Physics, or equivalent practical experience.
Willingness to travel up to 50% to customer sites — flexible based on engagement needs.
Preferred
Experience with AI accelerators or custom silicon (TPUs etc.)
CUDA / low-level GPU programming
Familiarity with VLLM / SGLang
Enterprise AI deployments in regulated industries