Gradial

AppliedAIEngineer

$130–220k Seattle, Washington, United States; Portland, Oregon, United States; Springfield, Illinois, United States; Toronto, Ontario, Canada; Bangalore, Karnataka, India Remote Friendly
The Brief

“Applied AI Engineer at Gradial. Skills: Applied AI, Agentic systems, LLMs, Production AI integration. Build autonomous and semi-autonomous agent workflows that interact with browsers, codebases, and APIs to complete complex content operations. Evaluate model behavior in live environments and pioneer approaches for applying reinforcement learning to end-to-end agent systems”

What You'll Achieve.

automating website updates, design system migrations, and ongoing content optimization while preserving brand integrity across every touchpoint; design agents that reason, interact with environments and evolve through feedback; align AI behaviors with user expectations

Industry & Context.

Problems you'll solve

solve hard problems; ambiguous problems

What They're Looking For.

Must Have

4+ years of software engineering experience, at least a year working directly on AI/ML-powered applications, fluency in AI tooling (e. g. OpenAI APIs, Anthropic APIs), Practical understanding of LLMs, prompt engineering and agentic architectures, Experience building production systems that integrate external environments - code execution, browser automation or API orchestration, High degree of ownership and comfort in experimental, fast-moving environments

Nice to Have

Familiarity with benchmarks like SWE-bench or evaluation strategies for tool-using agents, Exposure to customer-facing roles or user research for AI product development, Background in reinforcement learning or data-centric AI approaches

What You'll Do.

Build autonomous and semi-autonomous agent workflows that interact with browsers

and APIs to complete complex content operations

Evaluate model behavior in live environments and pioneer approaches for applying reinforcement learning to end-to-end agent systems

Design modular frameworks for tools

and evaluators to enable continuous learning and improvement of agents

Develop data pipelines to collect

and annotate real-world usage data for SFT and RL training

Design and build proprietary agent libraries

prompting strategies and benchmarking frameworks

How You'll Work.

Team & Collaboration

collaborate across product, research, and engineering; Partner with customers and internal stakeholders

Free ATS check

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