Solutions Architect, Customer Success - US (Remote)

Role overview

This is a customer-facing technical role on a Customer Success Engineering team that helps enterprise organizations operationalize trustworthy AI through an AI observability platform. The position bridges complex machine learning and large language model systems with real business value, serving as both a hands-on technical expert and a trusted advisor. It is remote within the US, with hybrid expectations for Bay Area candidates, and focuses on driving adoption, integration, and renewal outcomes.

Responsibilities

- Partner with delivery managers to architect and implement onboarding solutions aligned to customer goals - Build trusted advisor relationships with data science and ML engineering teams throughout the customer journey - Lead technical engagements including status syncs, roadmap discussions, QBRs, and escalations - Champion the customer voice across product and engineering to influence platform roadmap and strategy - Master the AI observability platform and help customers apply best practices for model transparency, monitoring, and compliance - Write custom integration code connecting the platform to customer data ecosystems using tools like Snowflake, Airflow, MLflow, S3, and Kafka - Build accelerators and reusable assets that speed adoption across the customer base

Requirements

- Demonstrated experience in customer-facing technical roles such as solutions architect, customer success engineer, or technical account manager - Ability to design enterprise-grade integrations across complex data ecosystems - Strong communication skills with the confidence to engage executive stakeholders and data science teams - Cross-functional collaboration experience across product, engineering, and delivery organizations - Customer-first mindset with curiosity, empathy, and a strong sense of ownership - Passion for continuous learning and willingness to lead through technical credibility

Nice to have

- Understanding of data science concepts, model interpretability, and explainability techniques - Working knowledge of data and workflow tools such as Hadoop, MongoDB, Snowflake, BigQuery, Spark, Kafka, Kinesis, Airflow, MLflow, Kubeflow, or Argo - Experience with machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn - Proficiency with Kubernetes and cloud platforms such as AWS, Azure, or GCP - Familiarity with the ML lifecycle including feature generation, training, deployment, monitoring, and evaluation - Familiarity with generative AI, large language models, RAG architectures, and agent-based systems

Benefits and work setup

US locations: $160,000 to $200,000 plus equity. San Francisco, New York City, and Seattle: $190,000 to $230,000 plus equity. Bay Area candidates work hybrid from the Palo Alto office three days per week. Benefits include unlimited PTO, premium health, dental, and vision coverage with 100% premium coverage for employees, a 401(k) plan, monthly fitness reimbursement, and paid parental leave. Palo Alto HQ perks include an annual Caltrain pass, monthly in-office massages, Fastrak reimbursement, and lunch provided Monday through Thursday.