Solutions Architect

Role overview A human data partner for AI teams is hiring a Solutions Architect to own the technical relationship across the full customer lifecycle for its enterprise data labeling and evaluation platform. The role partners with Account Executives on strategic deals and continues through onboarding, integration, and long-term optimization, acting as the trusted technical advisor for ML Engineers, Data Scientists, and MLOps teams using a deeply technical open-source-rooted product.

Responsibilities - Partner with Account Executives on strategic deals, leading technical discovery, tailored demos, architecture reviews, and proof-of-concepts that prove out the enterprise platform against real customer data and workflows. - Drive technical onboarding, guiding customers through installation, secure configuration, and best-practice deployment across cloud, on-prem, or hybrid environments. - Architect integrations between the labeling platform and customer AI and ML workflows, pipelines, storage, and enterprise systems. - Build custom solutions including scripts, plug-ins, and APIs to extend the platform for unique customer requirements. - Serve as the senior technical escalation point for assigned accounts, resolving advanced issues in collaboration with Product, Engineering, and Support. - Deliver enablement, workshops, and documentation that make customer teams self-sufficient on the platform.

Requirements - 5+ years in a customer-facing technical role such as Solutions Architect, Sales Engineer, or Professional Services Engineer for a highly technical product, ideally enterprise SaaS or ML and AI platforms; experience across both pre-sale and post-sale is ideal. - Hands-on experience building a data labeling pipeline, including data annotation, labeling workflows, or the broader ML lifecycle, with the ability to explain it to both engineers and executives. - Practical experience integrating SaaS platforms with ML pipelines, including deploying models to production or preparing datasets for data science teams. - Fluency in Python, REST APIs, and infrastructure tools such as Linux or Unix and Docker, with Kubernetes a plus; experience building against multiple client APIs and SDKs, and JavaScript, CSS, and HTML as a plus. - A track record of running technical discovery, demos, and proof-of-concepts in the sales cycle, then owning onboarding, implementation, troubleshooting, and custom development after the close. - Executive presence, sharp written communication, and the range to move between Data Scientists, ML Engineers, and technical executives while juggling multiple complex accounts.

Benefits and work setup - Base salary targeted between $122,500 and $143,500 USD depending on experience and skills, with variable compensation and anticipated on-target earnings of $175,000 to $205,000 USD. - Stock options, comprehensive health benefits, and a team culture rooted in transparency and collaboration. - Hiring across North America.