Applied AI Engineer

Gradial•Seattle, WA

12d•Hybrid

About The Position

Gradial is the marketing operations system of work that helps marketers and creatives move from idea to execution faster. Our platform orchestrates across martech stacks, workflows, and people to automate marketing execution, cutting execution time, so marketers focus on the work only humans can do: shaping brands, understanding customers, and creating the work that moves people. Backed by leading investors, we’re building software that adapts to the user, not the other way around. We move with urgency, operate with ownership, and solve hard problems from first principles. If you want to do ambitious work, take real responsibility, and help define the future of AI-native content operations, you’ll do your best work here. The Role We’re looking for an Applied AI Engineer to help build the next generation of agentic systems. This role combines deep software engineering skills with an experimental mindset around how AI can be practically applied to solve real user problems. You’ll collaborate across product, research, and engineering to design agents that reason, interact with environments and evolve through feedback.

Requirements

4+ years of software engineering experience, with at least a year working directly on AI/ML-powered applications.
Strong 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 Haves

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.

Responsibilities

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.
Design modular frameworks for tools, memory, and evaluators to enable continuous learning and improvement of agents.
Develop data pipelines to collect, transform, and annotate real-world usage data for SFT and RL training.
Design and build proprietary agent libraries, prompting strategies and benchmarking frameworks.
Partner with customers and internal stakeholders to understand edge cases and align AI behaviors with user expectations.