1 Π΄Π΅Π½Ρ Π½Π°Π·Π°Π΄
Applied AI Engineer (AI)
130Β 000 - 220Β 000$
ΠΡΡΡ & Π‘ΠΎΠΏΡΠΎΠ²ΠΎΠ΄
ΠΠ»Ρ ΠΌΡΡΡΠ° Ρ ΡΡΠΎΠΉ Π²Π°ΠΊΠ°Π½ΡΠΈΠ΅ΠΉ Π½ΡΠΆΠ΅Π½ Plus
ΠΠΏΠΈΡΠ°Π½ΠΈΠ΅ Π²Π°ΠΊΠ°Π½ΡΠΈΠΈ
Π’Π΅ΠΊΡΡ:
TL;DR
Applied AI Engineer (AI/LLM Agents): Building autonomous and semi-autonomous agent workflows, evaluation systems, and data pipelines for AI-native marketing content operations with an accent on production LLM applications, tool use, and agentic architectures. Focus on designing agents that interact with browsers, codebases, and APIs, applying reinforcement learning to end-to-end systems, and enabling continuous improvement through tools, memory, and evaluators.
Location: Seattle, WA, USA
Salary: $130,000β$220,000 annually, plus potential bonuses and equity
Company
Gradial builds a marketing operations platform that orchestrates martech stacks, workflows, and people to automate marketing execution.
What you will do
- 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 develop reinforcement learning approaches for end-to-end agent systems.
- Design modular frameworks for tools, memory, evaluators, prompting, and proprietary agent libraries.
- Develop data pipelines to collect, transform, and annotate usage data for supervised fine-tuning and reinforcement learning.
- Create benchmarking frameworks and collaborate with customers, product, and engineering stakeholders to address edge cases and align AI behavior with user expectations.
Requirements
- 4+ years of software engineering experience, including at least 1 year working directly on AI/ML-powered applications.
- Strong fluency with AI tooling such as OpenAI APIs and Anthropic APIs.
- Practical understanding of LLMs, prompt engineering, and agentic architectures.
- Experience building production systems integrating external environments, including code execution, browser automation, or API orchestration.
- Strong ownership and comfort working in experimental, fast-moving environments.
Nice to have
- Familiarity with SWE-bench or evaluation strategies for tool-using agents.
- Customer-facing experience or user research experience in AI product development.
- Background in reinforcement learning or data-centric AI.
Culture & Benefits
- Performance-based annual bonus and meaningful equity awards.
- Medical, dental, and vision insurance, fully covered for employees and 75% covered for dependents.
- Fertility and family-planning support.
- 401(k) retirement plan, unlimited PTO, and paid sick leave.
- Seattle office with a gym, sauna, bike parking, and free lunch three days per week.
- Flexible work hours and a collaborative environment focused on learning, ownership, and impact.
ΠΡΠ΄ΡΡΠ΅ ΠΎΡΡΠΎΡΠΎΠΆΠ½Ρ: Π΅ΡΠ»ΠΈ ΡΠ°Π±ΠΎΡΠΎΠ΄Π°ΡΠ΅Π»Ρ ΠΏΡΠΎΡΠΈΡ Π²ΠΎΠΉΡΠΈ Π² ΠΈΡ ΡΠΈΡΡΠ΅ΠΌΡ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡ iCloud/Google, ΠΏΡΠΈΡΠ»Π°ΡΡ ΠΊΠΎΠ΄/ΠΏΠ°ΡΠΎΠ»Ρ, Π·Π°ΠΏΡΡΡΠΈΡΡ ΠΊΠΎΠ΄/ΠΠ, Π½Π΅ Π΄Π΅Π»Π°ΠΉΡΠ΅ ΡΡΠΎΠ³ΠΎ - ΡΡΠΎ ΠΌΠΎΡΠ΅Π½Π½ΠΈΠΊΠΈ. ΠΠ±ΡΠ·Π°ΡΠ΅Π»ΡΠ½ΠΎ ΠΆΠΌΠΈΡΠ΅ "ΠΠΎΠΆΠ°Π»ΠΎΠ²Π°ΡΡΡΡ" ΠΈΠ»ΠΈ ΠΏΠΈΡΠΈΡΠ΅ Π² ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΡ. ΠΠΎΠ΄ΡΠΎΠ±Π½Π΅Π΅ Π² Π³Π°ΠΉΠ΄Π΅ β
ΠΠΎΡ ΠΎΠΆΠΈΠ΅ Π²Π°ΠΊΠ°Π½ΡΠΈΠΈ
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