Solutions EngineerLocation: North America Remote / San Francisco 3 Full-Time
About the roleWe're a seed-stage AI infrastructure startup powering large-scale training and inference for frontier labs, AI-native startups, and enterprises. We're hiring a Sales Engineer (SE) to lead technical discovery, run world-class demos and POCs, and partner with Sales to drive successful evaluations and expansions across strategic accounts.
This is a pre-sales-forward role for someone who can translate customer needs into crisp technical validation, build trust with executives and engineers, and create a repeatable technical sales motion across our stack (hardware + software).
What you'll do- Partner with Sales to qualify opportunities, lead technical discovery, and shape
evaluation plans tied to customer success criteria. - Design and deliver compelling demos of our full stack-tailoring messaging for both technical and executive audiences.
- Own the technical POC process: define scope, success metrics, architecture, timeline, and stakeholder alignment; ensure clean handoffs into production.
- Build strong relationships with customer stakeholders, including engineering
leaders, platform teams, and security/infra owners. - Translate technical requirements into solution designs that map directly to business outcomes (latency, throughput, cost, reliability, security).
- Produce high-quality enablement assets (demo environments, evaluation playbooks, reference architectures, FAQs) to scale the team's effectiveness.
- Provide high-signal feedback to Product/Engineering/Research based on recurring objections, feature gaps, and competitive dynamics.
What We're Looking For
Minimum qualifications - 5+ years in a customer-facing technical role, including 2+ years in a pre-sales function
(Sales Engineer, Solutions Architect, etc.). - Proven ability to run technical evaluations end-to-end and influence outcomes in
complex sales cycles. - Excellent communication skills: can explain infrastructure and AI concepts clearly to both technical and non-technical audiences.
- Strong knowledge of AI/ML infrastructure and GPU-based systems, including how they integrate into HPC environments.
- Strong understanding of training, fine-tuning, and inference for open-source LLMs.
- Proficiency in Python and JavaScript, with experience building prototypes on API
platforms. - Familiarity with Kubernetes, SLURM, Docker/containers, and infrastructure
automation/IaC (e.g., Ansible). - High ownership, comfort in ambiguity, and strong prioritization across multiple deals and customer threads.
Nice to have - Experience selling or supporting AI infrastructure products (compute, orchestration, model serving, observability).
- Experience with performance benchmarking, cost modeling, and evaluation frameworks for GenAI systems.
- Prior work at a fast-moving startup with a 2build the plane while flying it2 mentality.
- POCs convert reliably because evaluations are well-scoped, technically credible, and tied to customer ROI.
What success looks like - POCs convert reliably because evaluations are well-scoped, technically credible, and tied to customer ROI
- Sales cycles accelerate through clear technical validation and strong stakeholder trust.
- Your demos and enablement assets become repeatable foundations for the broader go-to-market team.
