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Scale AI

Supplies AI training data and builds AI apps

Staff Solutions Architect - Enterprise

Pre-Sales Solution EngineeringConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • Deep engineering background with substantial experience in customer-facing technical roles at enterprise scale, such as solutions architecture, sales engineering, forward-deployed engineering, or technical consulting; hands-on proficiency in Python or an equivalent language.
  • Track record of owning technical outcomes on complex, multi-stakeholder deals with Fortune 500 or equivalent enterprises.
  • Strong systems thinking across cloud, data, and AI/ML architecture, with production experience in Generative AI and LLM systems, including agentic systems and their reliability characteristics.
  • Demonstrated ability to define evaluation methodology for problems without established metrics and negotiate quality bars that withstand customer validation and regulatory review.
  • Experience operating as a senior technical advisor to executive stakeholders, with the ability to disagree with an executive while retaining credibility.
  • Evidence of technical work adopted beyond one's own engagements, such as reference architectures, methodologies, processes, or tooling used by others.
  • Experience mentoring technical individual contributors and raising a team's standard.
  • Clear technical communication and problem-solving when requirements are unknown, including stating what is not yet known and how it would be determined.
  • Deep domain expertise in financial services, healthcare/life sciences, or consumer verticals.

Responsibilities

  • Lead technical strategy and execution on the largest and most complex deals in the assigned vertical, measured by revenue, product scope, or data volume; act as the final technical authority on whether the company can deliver what is proposed.
  • Own qualification on ambiguous, multi-stakeholder opportunities where requirements are unresolved, and make and defend go/no-go and disqualification recommendations on deals with significant revenue attached.
  • Define evaluation methods for use-case classes in the assigned vertical, including evaluation datasets, baselines, quality dimensions, and acceptance criteria that withstand customer and regulatory scrutiny; establish approaches other Solutions Architects can reuse.
  • Own long-term technical relationships with C-level and senior technical stakeholders across multiple accounts, including CTOs, Heads of AI, and engineering leadership, and convert those relationships into sustained technical partnership and expansion.
  • Lead workshops, technical deep dives, and executive presentations on strategic opportunities; build or direct prototypes that make complex solutions tangible.
  • Build reference architectures, demo environments, discovery patterns, and solution blueprints that become standard practice across the team; where appropriate, drive solutions that graduate into the company's product and solution stack.
  • Create or change the team's technical processes, including qualification standards, workshop practice, and scoping approach; drive initiatives that improve win rates, shorten cycles, or increase the scalability of the Solutions Architecture function.
  • Synthesize patterns across accounts into prioritized, evidence-backed input to Product and Engineering, and contribute to strategic roadmap decisions rather than submitting individual feature requests.
  • Mentor other Solutions Architects, raise the team's technical standard, serve on hiring panels, and document and distribute learnings so they do not have to be relearned.

Desired Qualifications

  • External recognition in the domain through conference speaking, published technical writing, industry forums, or customer advisory participation.
  • Experience with domain compliance requirements, including model risk management and fair lending (FSI), HIPAA, clinical safety and validated systems (HCLS), or high-volume quality and unit-economics measurement (Consumer).
  • Background in forward-deployed engineering or shipping production machine-learning systems.
  • Experience creating large-scale evaluation datasets alongside domain experts.
  • Experience creating or scaling a solutions architecture practice, playbook, or vertical from an early stage.

About the Company

Scale AI

Supplies AI training data and builds AI apps

Scale AI supplies the data that AI labs use to train and test their models, and it builds custom AI applications for companies and government agencies such as the Pentagon. It first made its name labeling camera and sensor data for self-driving car companies like Waymo, then worked with OpenAI on InstructGPT, the 2022 forerunner of ChatGPT. Freelancers, some of them doctors and lawyers, write, label, and grade examples through its Outlier platform, and labs pay Scale for this data and for tests of model quality. Clients pay Scale to build AI tools on their own data, and some of those tools run on classified military networks. Alexandr Wang and Lucy Guo founded Scale AI in 2016, and it is based in San Francisco.

Company size
1,001-5,000
Company stage
Acquired
Total funding
$1.6B
Headquarters
San Francisco, CA
Founded
2016

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