Senior Applied AI Engineer (Agentic Systems)

Clera•San Francisco, CA

3d•$185,000 - $210,000•Hybrid

About The Position

This is a founding-level, senior individual contributor role at a Series A AI compliance SaaS company building the system of record for enterprise marketing and packaging review. The platform automates high-stakes compliance workflows — turning manual, multi-week approval cycles into hours — for large regulated-industry brands. As a Senior Applied AI Engineer focused on Agentic Systems, you will own the core technical moat: the deterministic orchestration layer and safety infrastructure that makes enterprise-grade AI compliance trustworthy at scale. You are not building demos — you are building auditable, production-grade systems where correctness is non-negotiable. This is a hybrid, in-office role based in San Francisco, CA (3 days/week in-office). You will work directly alongside a small, senior engineering team at a critical architectural inflection point.

Requirements

7+ years of professional software/ML engineering experience
Minimum 2-year average tenure across roles, with a clear track record of career progression and promotions
Demonstrated expertise architecting multi-agent AI systems: agent role design, multi-step reasoning flows, tool integration, memory/retrieval architectures, and deterministic/auditable workflows
Hands-on experience with LLM orchestration frameworks and stateful workflow engines (e.g., Temporal, Prefect, or equivalent)
Strong background in retrieval-augmented generation (RAG) and vector/graph-based retrieval systems
Experience building and owning production-grade evaluation and safety frameworks for AI systems
Startup experience or equivalent experience in CPG, regulated industries, or enterprise compliance domains
Must be eligible to work in the United States without visa sponsorship
Able and willing to work in-office in San Francisco, CA at least 3 days per week

Nice To Haves

Experience with vision-language models (VLMs), OCR pipelines, or document layout analysis
Background in marketing technology, packaging compliance, or regulatory tech (regtech)
Familiarity with enterprise compliance workflows or brand governance processes
Prior experience as a founding or early-stage engineer

Responsibilities

Design and evolve multi-agent LLM systems that decompose complex review tasks into reliable, auditable steps. Define agent responsibilities, hand-offs, and termination conditions to minimize reasoning drift and maximize consistency.
Architect retrieval pipelines using RAG, structured memory, and graph-based retrieval approaches to supply agents with the right context at the right time. Balance recall, precision, and latency across large knowledge bases (brand guidelines, regulations, historical decisions).
Own long-running, fault-tolerant workflows using Temporal (or similar), ensuring retries, versioning, and determinism across non-deterministic model calls. Treat agent orchestration as a distributed systems problem — managing state, failures, and observability.
Build evaluation frameworks using statistical metrics, gold labels, and automated regression testing to prove system reliability. Prioritize correctness and trust, especially in high-risk legal and compliance scenarios.
Collaborate on image and document preprocessing (OCR, layout analysis, Vision Language Models) to ensure downstream agents receive structured, machine-readable context.