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redacted · Global Head of Forward Deployed Engineering, OpenAI Name revealed in your package
He leads the global FDE practice this role sits in. A note that shows you already run the FDE motion, scoping, delivery, and eval-driven feedback, speaks his language directly.
Paste into the LinkedIn connect note (277 chars, limit 300).
Send once they accept, or as an InMail.
redacted · Forward Deployed Engineering (hiring for FDE roles), OpenAI In your package
Publicly hiring FDEs across SF, NYC, and Europe. Closest to the open req itself, so a short note here can move an application out of the pile.
redacted · Forward Deployed Engineering, OpenAI In your package
A practitioner on the team. Peers reply more often than execs and can give you the real read on the work and refer you in.
Written from scratch for this exact posting, in the candidate's voice. Delivered as PDF plus paste-ready text.
Re: Forward Deployed Engineer (FDE), SF · OpenAI
Dear OpenAI Hiring Team,
OpenAI's Forward Deployed Engineering team is doing the version of my job I most want to be doing: taking frontier models into the hands of your most strategic customers and owning the outcome. For the last three years at Copperline AI I've run exactly this motion for enterprise clients: discovery with stakeholders, technical scoping, system design, build, and production rollout, with adoption and measured workflow impact as the bar for done.
The parts of the role about evals and field feedback are where I'd add value fastest. Every engagement I run ships with a graded eval suite and regression gates in CI. That discipline caught three model-upgrade regressions before any customer saw them, and it's how I raised answer accuracy 14 points while cutting p95 retrieval latency from 900ms to under 200ms on a customer-facing assistant. I write production Python and TypeScript daily and I've carried the pager for the systems I ship.
I'm in San Francisco, the hybrid cadence works, and heavy travel is fine. Embedding with customer teams is the part of the work I like most. I'd love to show you how I scope and land a deployment.
Sincerely,
Jordan Avery
The candidate's real experience, rewritten for this role: the summary keys to the job description, the accomplishments lead with what this team cares about.
San Francisco, CA (sample profile)
Applied AI engineer with eight years across backend, data, and ML systems and the last three shipping LLM products for enterprise clients. I've owned delivery end to end: scoping with stakeholders, building RAG and agent systems in Python and TypeScript, and proving quality with graded evals wired into CI. I'm at my best embedded with a customer team, turning an ambiguous problem into a production system with numbers behind it.
Applied AI Engineer · Copperline AI (2023 to Present)
LLM-powered products for mid-market and enterprise clients.
Machine Learning Engineer · Halepoint (2022 to 2023)
Fraud models, feature store, and low-latency scoring at 3,000 requests a second.
Senior Software Engineer, Data Platform · Northslope Data (2019 to 2022)
Led a 3-engineer pod: Kafka, Spark, and Airflow pipelines on AWS.
Software Engineer · Riverbend Software (2017 to 2019)
Backend services in Python and Postgres for a logistics SaaS.
BS, Computer Science, University of Colorado Boulder, 2017 · AWS Certified Solutions Architect, Associate
Every document also arrives as paste-ready text, so nothing gets locked inside a PDF.
CONTACT -- OpenAI : Forward Deployed Engineer (FDE) - SF PRIMARY TARGET [revealed in your package] - Global Head of Forward Deployed Engineering, OpenAI CONNECTION REQUEST (277 chars, limit 300) --- [name], I'm applying for the FDE role in SF. I've spent the last three years shipping LLM systems for enterprise clients: RAG, agent workflows, and the eval harnesses that keep them honest. Owning delivery from scoping to production is the work I love. Would be glad to connect. --- FOLLOW-UP MESSAGE, ALTERNATE TARGETS, AND SEND CADENCE [shown in full in your package]
Jordan Avery San Francisco, CA Re: Forward Deployed Engineer (FDE) - SF, OpenAI Dear OpenAI Hiring Team, OpenAI's Forward Deployed Engineering team is doing the version of my job I most want to be doing: taking frontier models into the hands of your most strategic customers and owning the outcome. For the last three years at Copperline AI I've run exactly this motion for enterprise clients: discovery with stakeholders, technical scoping, system design, build, and production rollout, with adoption and measured workflow impact as the bar for done. The parts of the role about evals and field feedback are where I'd add value fastest. Every engagement I run ships with a graded eval suite and regression gates in CI. That discipline caught three model-upgrade regressions before any customer saw them, and it's how I raised answer accuracy 14 points while cutting p95 retrieval latency from 900ms to under 200ms on a customer-facing assistant. I write production Python and TypeScript daily and I've carried the pager for the systems I ship. I'm in San Francisco, the hybrid cadence works, and heavy travel is fine. Embedding with customer teams is the part of the work I like most. I'd love to show you how I scope and land a deployment. Sincerely, Jordan Avery
Jordan Avery Applied AI Engineer - LLM Applications, RAG, Agents, Evaluation San Francisco, CA (sample profile) Summary Applied AI engineer with eight years across backend, data, and ML systems and the last three shipping LLM products for enterprise clients. I've owned delivery end to end: scoping with stakeholders, building RAG and agent systems in Python and TypeScript, and proving quality with graded evals wired into CI. I'm at my best embedded with a customer team, turning an ambiguous problem into a production system with numbers behind it. Key Accomplishments - Shipped 6 production LLM applications in three years at Copperline AI: retrieval assistants, agent workflows, and document pipelines used daily by teams at insurance, legal, and logistics clients. - Cut p95 retrieval latency on a customer-facing RAG assistant from 900ms to under 200ms while raising answer accuracy 14 points on a 1,200-case graded eval suite. - Built the company's evaluation harness, now standard on every client engagement; it caught 3 model-upgrade regressions before any customer saw them. - Led the 3-engineer data-platform pod at Northslope Data moving roughly 2 billion events a day at a 99.95% delivery SLO. [full experience, education, and skills sections continue as delivered]
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