S

Scale AI

Supplies AI training data and builds AI apps

Frontier Agents Engineer - Forward Deployed Engineering

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USA$180k - $225k

About the Job

Requirements

  • At least 4 years of software engineering experience, with strong fundamentals in distributed systems, data structures, algorithms, and system design.
  • Strong Python programming skills and experience building production software.
  • Experience building or deploying AI-powered applications using modern large language model application programming interfaces, agent frameworks, Model Context Protocol, retrieval systems, or vector databases.
  • Experience with cloud platforms such as Amazon Web Services, Azure, or Google Cloud Platform and modern production infrastructure.
  • Ability to navigate ambiguous technical requirements and rapidly iterate toward production solutions.
  • Ability to work directly with enterprise engineering teams.

Responsibilities

  • Architect and deploy production AI systems integrating with enterprise cloud platforms, data warehouses, internal application programming interfaces, business applications, and proprietary software.
  • Design scalable agent architectures combining large language models, retrieval, memory, tools, structured knowledge, and enterprise data in production workflows.
  • Build integrations that enable AI agents to interact safely with customer systems and meet security, governance, and compliance requirements.
  • Prototype AI capabilities and evolve successful prototypes into production-ready systems.
  • Build production infrastructure, agent runtimes, orchestration frameworks, context pipelines, execution services, and tool integrations for enterprise AI systems.
  • Engineer systems for reliability, observability, latency, scalability, retries, fallback strategies, and graceful degradation.
  • Design human-in-the-loop workflows combining AI automation with expert oversight.
  • Build deployment patterns that enable safe evolution through continuous delivery and experimentation.
  • Operationalize AI quality systems to keep production agents reliable as models, prompts, and customer data evolve.
  • Deploy evaluation harnesses using offline benchmarks, online experiments, golden datasets, regression suites, and large language model-as-a-judge methods to detect quality regressions.
  • Implement tracing, observability, monitoring, guardrails, grounding, and safety mechanisms for production AI systems.
  • Partner with Applied AI engineers to productionize evaluation methodologies, retrieval strategies, reasoning architectures, and emerging AI capabilities.
  • Evaluate newly released models, agent frameworks, evaluation methodologies, and developer tooling for safe adoption in production systems.
  • Work directly with enterprise customers to understand their technical infrastructure, software architecture, and operational workflows.
  • Translate ambiguous customer problems into scalable production AI architectures.
  • Collaborate with customer software engineers, machine learning engineers, platform teams, and product organizations to deploy AI in mission-critical workflows.
  • Identify reusable engineering patterns that can become capabilities across multiple enterprise deployments.
  • Serve as the primary technical advisor for strategic enterprise accounts.
  • Lead architecture discussions across distributed systems, AI infrastructure, enterprise integration, and production deployment.
  • Document reusable architecture patterns, deployment strategies, integration frameworks, and operational best practices.
  • Work with product, infrastructure, and Applied AI teams to improve the platform.
  • Architect enterprise AI systems and agent platforms; build integrations across cloud infrastructure and enterprise software; deploy agents into production; operationalize evaluation frameworks, guardrails, and observability; run production experiments and measure real-world business impact; and improve deployed systems using customer feedback and operational telemetry.

Desired Qualifications

  • Experience deploying production AI agents or autonomous systems.
  • Experience designing distributed systems, application programming interfaces, orchestration services, or large-scale backend systems.
  • Experience with cloud-native infrastructure, Docker, Kubernetes, Infrastructure as Code, and continuous integration and continuous delivery.
  • Experience integrating AI systems into enterprise software environments.
  • Familiarity with modern agent architectures, retrieval systems, tool use, memory, and context engineering.
  • Experience with evaluation frameworks, large language model observability, regression testing, tracing, and AI monitoring.
  • Experience implementing guardrails, grounding, and safety mechanisms for production AI systems.
  • Experience operationalizing new foundation models, agent frameworks, or AI infrastructure.
  • Experience working directly with enterprise customers.
  • Ability to translate complex technical requirements into scalable production systems.
  • Experience leading architecture reviews, technical workshops, or customer design sessions.

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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