Senior Forward Deployed Engineer
💰 $156,060 – $211,140/yr
At a glance
- Who can apply
- United States
- Work arrangement
- Hybrid · San Francisco, CA
- Employment type
- Full-time
- Seniority
- Senior
- Salary
- $156,060 – $211,140/yr · published by the employer
Checked against the employer's source on October 15, 2026. Details come from the employer's posting; confirm them on the application page.
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Job Description
About Afresh
Afresh is the AI platform for grocery retail. Leveraging proprietary AI designed for high-volatility environments, we empower enterprise partners like Albertsons, Meijer, and Wakefern to drive smarter decisions across their entire enterprise. Following 70% revenue growth in 2025, we've scaled to 6 enterprise-grade solutions live in over 10% of the U.S. grocery market. Our platform orchestrates billions of decisions and prevented over 200 million pounds of food waste last year.
About the Role
As a Senior Forward Deployed AI Engineer, you combine field deployment with platform development. You'll spend dedicated time embedded with enterprise customers integrating their data and shipping AI systems, then return to the platform team to harden those solutions into reusable tooling. This is senior, hands-on 0-to-1 work with no established playbook.
Responsibilities – Field (Forward Deployed)
- Partner with account leads and customer technical teams to scope, architect, and define deployment paths including data sources, system architecture, and production readiness
- Embed with customer data and engineering teams (remote and on-site); integrate into their cloud and data platforms; build production-grade pipelines
- Transform messy enterprise data into trustworthy data products
- Design and ship LLM- and agent-powered systems: retrieval, agentic workflows, data-quality and analytics agents—production-ready, not demo-focused
Responsibilities – Platform (Building Infrastructure)
- Harden field-proven solutions into shared platform capabilities: knowledge graph, ontology, retrieval layer, agent frameworks, and serving infrastructure
- Build evaluation, tracing, and monitoring tooling to measure quality (accuracy, hallucination rate, latency, cost) and accelerate team velocity
- Build for leverage: create abstractions and tools that scale across multiple customer deployments
Compensation & Details
- Salary: $156,000–$211,000 USD annually
- Work Style: Hybrid (field deployment + platform development)
- Focus Areas: LLMs, AI agents, data integration, enterprise infrastructure, production ML systems
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