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Associate Forward Deployed Engineer

Eliza -
Remote

Quick Apply

Job Details

Full-time
6 days ago

Benefits

Work from home stipend

Qualifications

Software engineering
Public Cloud
Version control systems
Continuous integration

Full Job Description

Associate Forward Deployed Engineer

About Us

Eliza is a technology services company and Advanced-tier OpenAI partner that’s dedicated to helping organizations build and deploy cutting-edge AI solutions. From generative AI and custom LLM integrations to predictive analytics and intelligent automation, we work across industries to bring real-world AI applications to life. Our projects combine deep technical expertise with hands-on client collaboration to solve high-impact problems.

Role Overview

The Associate Forward Deployed Engineer (Associate FDE) is an entry point to our delivery team that grows into full ownership of client-facing AI engagements. Associate FDEs work alongside our Lead and Senior Forward Deployed Engineers on live client work—contributing directly to the technical build, joining client conversations from requirements gathering through proof-of-concept and delivery, and taking on scoped client-facing assignments as readiness grows.

You will rotate across engagements rather than sitting on one account, which means exposure to several senior engineers, several industries, and several kinds of hard problems in your first year. Alongside client work, Associate FDEs identify, build, and present internal AI use cases that improve how Eliza itself operates—real problems you own end-to-end, with real users.

This is a deliberate entry point into forward-deployed engineering. The role is structured so that engineers who consistently meet our delivery bar build toward independently owning client engagements.

Key Responsibilities

1. Contribute to Client-Facing Solution Delivery

Build and test components of custom AI systems—model integrations, data pipelines, APIs, and deployment tooling—under senior technical direction.
Participate in client sessions across the engagement lifecycle: requirements gathering, prototype reviews, technical working sessions, and delivery readouts.
Take direct ownership of scoped client-facing assignments, with responsibility growing over your first several months.
Prototype rapidly and iterate against real client feedback.

2. Own Internal AI Use Cases

Proactively identify opportunities to apply AI within Eliza's own operating rhythms.
Scope, build, and present high-impact internal solutions end-to-end.
Use that work to build the judgment, communication, and delivery instincts client ownership requires.

3. Build Technical Craft

Write clean, reviewable code and respond productively to technical review.
Learn and apply Eliza's delivery standards for quality, documentation, and pace.
Develop working fluency with LLM integration, prompt design, evaluation, and cloud deployment.

4. Escalate Clearly and Early

Surface complex technical challenges, blockers, and risks with clear context.
Know the difference between a problem you should push through and one that needs senior input.
Keep the engagement team informed on status without needing to be chased.

5. Learn Through Direct Mentorship

Work closely with Lead and Senior FDEs, absorbing different approaches to technical and client problems.
Seek and act on direct feedback about technical execution, client communication, and ownership.
Build the client-management nuance that separates good engineering from good delivery.

Qualifications

Required

0–2 years of software engineering experience, or equivalent demonstrated ability through internships, open-source contributions, or substantial personal projects.
Strong programming skills in Python; familiarity with JavaScript/TypeScript, Go, or similar is a plus.
Clear written and verbal communication, and real interest in working directly with clients.
Working exposure to modern cloud platforms (AWS, GCP, or Azure) and version control / CI workflows.
Evidence of shipping something real—a working system, tool, or model that other people used.
High ownership instinct: you finish things, and you ask for help before a problem becomes a crisis.

Preferred

Hands-on experience with LLMs (e.g., Anthropic, OpenAI, Cohere), vector search, or prompt engineering.
Any prior client-facing, consulting, or professional services exposure.
Familiarity with ML/AI project work, independently or alongside data science teams.
Comfort with ambiguity and rapid context-switching across problem domains.

What We Offer

Competitive compensation (salary + annual bonus).
Equity options in a growing AI services company.
Fully remote work
Remote work perks include a WFH stipend and monthly lifestyle stipend
Direct mentorship from senior engineers on live client work, starting immediately.
Exposure to multiple industries, clients, and problem domains in your first year.
A collaborative, mission-driven team passionate about the real-world impact of AI.

Quick Apply

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