S

Scale AI

Supplies AI training data and builds AI apps

Frontier Agents Engineer - Applied AI

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USAIn-person

About the Job

Requirements

  • At least 4 years of software engineering, machine learning, or applied artificial intelligence experience.
  • Strong Python programming skills.
  • Experience building production artificial intelligence systems using large language models.
  • Experience with modern artificial intelligence tooling, including OpenAI, Claude, MCP, agent frameworks, vector databases, or retrieval systems.
  • Strong understanding of machine learning fundamentals and modern language models.
  • Experience designing or evaluating artificial intelligence systems using quantitative metrics.
  • Ability to communicate effectively and work directly with enterprise customers.

Responsibilities

  • Design and deploy production artificial intelligence agents using advances in large language models, reasoning, retrieval, memory, and tool use.
  • Architect intelligent systems combining large language models, traditional machine learning, structured knowledge, enterprise data, and deterministic software into reliable production workflows.
  • Engineer customer intelligence layers, retrieval pipelines, memory systems, and knowledge representations for agents reasoning over heterogeneous enterprise data.
  • Develop multi-agent systems coordinating reasoning, planning, tool execution, and human oversight.
  • Translate frontier artificial intelligence research into production systems by evaluating models, prompting techniques, reasoning paradigms, and agent architectures.
  • Own the experimentation lifecycle from hypothesis generation through production rollout.
  • Design evaluation frameworks using offline benchmarks, online A/B experiments, golden datasets, regression suites, large-language-model-as-a-judge methods, and human evaluation.
  • Run controlled experiments and ablation studies to assess models, prompts, retrieval strategies, reasoning techniques, memory systems, and agent architectures.
  • Continuously evaluate newly released frontier models for improvements in quality, latency, reliability, or cost.
  • Develop confidence estimation, reflection, and continuous learning systems that improve agents using real-world feedback.
  • Build production-quality artificial intelligence systems emphasizing reliability, observability, latency, safety, and cost.
  • Design agent guardrails, fallback strategies, tracing, monitoring, and evaluation pipelines for safe deployment in high-stakes environments.
  • Collaborate with infrastructure engineers to deploy artificial intelligence systems securely within enterprise cloud environments.
  • Build human-in-the-loop workflows combining artificial intelligence automation with expert oversight.
  • Partner with enterprise customers to understand their business, data, and operational challenges.
  • Translate ambiguous customer problems into production artificial intelligence architectures.
  • Rapidly prototype ideas, validate them with customers, and evolve successful solutions into scalable production systems.
  • Identify reusable patterns that become core capabilities across enterprise deployments.
  • Design reasoning and agent architectures; build retrieval, memory, and customer intelligence systems; develop predictive models alongside large language models; run experiments and ablation studies; ship production systems into enterprise environments; measure business impact through online experimentation; and improve agents using real-world feedback.
  • Develop expertise integrating artificial intelligence systems into enterprise environments, deploying production infrastructure, and working directly with customer engineering teams.

Desired Qualifications

  • Experience building production artificial intelligence agents or autonomous systems.
  • Deep understanding of reasoning, retrieval, memory, planning, and tool use.
  • Experience designing evaluation frameworks for large language models and agentic systems.
  • Experience with retrieval-augmented generation, semantic search, knowledge graphs, customer intelligence systems, or structured knowledge representations.
  • Experience with fine-tuning, distillation, reinforcement learning, small language models, or model optimization.
  • Familiarity with multimodal artificial intelligence systems and frontier foundation models.
  • Experience building distributed production systems.
  • Experience with cloud platforms such as AWS, Azure, or GCP.
  • Experience with Docker, Kubernetes, continuous integration and continuous delivery, and production observability.
  • Experience integrating artificial intelligence systems into enterprise software environments.
  • Experience working directly with enterprise customers.
  • Ability to translate ambiguous business problems into technical architectures.
  • Strong written and verbal communication skills.
  • Experience leading technical workshops, architecture reviews, 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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