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Role Description

As a Senior Sales Engineer, you will serve as a technical bridge between customers and engineering teams, ensuring AI workloads are architected for scalability, efficiency, and production readiness. You will guide clients from initial discovery through proof-of-concept validation, translating their requirements into practical, economically viable solutions. This role combines deep technical expertise in AI infrastructure with strategic commercial insight, influencing revenue quality, engineering focus, and customer trust. You will collaborate closely with Sales and Engineering to align technical feasibility with business outcomes. The position demands a high degree of analytical rigor, strong communication skills, and the ability to operate effectively in fast-paced, high-stakes environments. Remote work within the U.S. is fully supported, offering flexibility while contributing to cutting-edge AI platform development.

• Lead in-depth technical discovery sessions with customer engineering teams to understand AI model requirements, latency constraints, throughput goals, and GPU resource considerations.
• Translate customer ambitions into production-ready architectures while identifying hidden technical risks and ensuring alignment with platform capabilities.
• Collaborate with Sales to influence deal strategy, prevent overcommitments, and improve PoC-to-production conversion rates.
• Define measurable success criteria for proofs-of-concept, including latency, throughput, time-to-first-token, and cost benchmarks.
• Align internal resources, including ML solution architects, engineering, and GPU infrastructure, to deliver scalable solutions.
• Identify recurring workload patterns and provide actionable insights to Product and Engineering teams to evolve platform capabilities.
• Serve as a trusted technical advisor to customers, shaping platform adoption and fostering long-term relationships.

Qualifications

• Deep expertise in AI inference systems and GPU-backed infrastructure.
• Experience with LLM workloads and performance-sensitive environments.
• Proficiency with inference frameworks and libraries such as vLLM, SGLang, TensorRT-LLM, and OpenAI/Anthropic SDKs.
• Ability to reason about architecture trade-offs across latency, throughput, and cost considerations.
• Strong customer engagement skills with the ability to challenge assumptions constructively.
• Commercial awareness and understanding of engineering resource allocation as a strategic factor.
• Programming proficiency in Python and familiarity with agentic pipeline frameworks (Langchain, Langsmith, smolagents, or equivalents).
• Experience with MLOps and DevOps tools such as Kubernetes, Docker, and Git; API frameworks like FastAPI or Flask.
• Knowledge of cloud platforms including AWS (SageMaker, Bedrock), GCP (Vertex AI), and Azure ML.
• Excellent communication, problem-solving, and cross-functional collaboration skills.

Benefits

• Competitive base salary ranging from $152,000 to $228,000, plus RSUs and performance bonuses.
• 100% company-paid medical, dental, and vision insurance for employees and their families.
• 401(k) plan with up to 4% company match and immediate vesting.
• Paid parental leave: 20 weeks for primary caregivers, 12 weeks for secondary caregivers.
• Company-paid short-term, long-term, and life insurance coverage.
• Remote work reimbursement up to $85/month for mobile and internet expenses.
• Flexible working arrangements and professional development opportunities.
• Dynamic, collaborative, and innovation-driven work environment with exposure to cutting-edge AI technologies.