Senior/Staff Applied AI Engineer, Agents

At Scale, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we're accelerating the development of autonomous AI agents in frontier labs through agentic data and evaluations, paving the road to Artificial General Intelligence (AGI).'

The Agent Capabilities & Environments (ACE) team, part of Scale's Research organization, brings together customer-facing Researchers and Applied AI Engineers. Our core mission includes benchmarking autonomous agent performance across real-world scenarios and environments, creating robust data programs to improve Large Language Models (LLMs) agentic capabilities, and building foundational tools and frameworks for evaluating models as agents. ACE focuses on autonomous agents that dynamically interact with diverse external environments, including code repositories, GUI interfaces, browsers, and more.

As a Senior/Staff Applied AI Engineer on the ACE team, you'll play a crucial role bridging state-of-the-art generative AI research, practical agent development, and the specialized data required to advance agentic systems.

You will:

• Develop frameworks and tools to benchmark and evaluate advanced agent capabilities.
• Construct realistic environments for training and evaluating autonomous agents.
• Design agent-focused data programs leveraging supervised fine-tuning (SFT) and reinforcement learning (RL) methodologies.
• Create robust data pipelines and novel agentic data types from diverse environments, including code repositories, web browsers, and computer systems.
• Collaborate closely with customers to understand requirements, guide model development, and achieve product objectives.
• Implement and adapt popular open-source agent libraries and benchmarks using proprietary datasets and models

Ideally you'd have:

• Min. 5+ years of practical experience building AI applications for real-world use cases.
• Strong engineering and AI fundamentals, supported by a Bachelors's and/or Master's degree or equivalent experience in Computer Science, Machine Learning, AI, or a closely related field.
• Deep understanding of modern deep learning methods, LLM technologies, and data-centric AI methodologies.
• Proven proficiency in Python, with experience writing, testing, and debugging code using standard data science libraries (e.g., NumPy, Pandas).
• Previous experience in customer-facing roles, effectively translating complex requirements into actionable development goals.
• Passion for solving ambiguous, complex technical challenges using cutting-edge research.

Nice-to-haves:

• Hands-on experience developing AI applications within the modern Generative AI stack (OpenAI APIs, commercial or open-source LLMs).
• Experience building autonomous agents that leverage external tools, produce structured outputs, and interact with various environments.
• Familiarity with agent benchmarking datasets such as SWE-Bench, tau-bench, and OS-World.