Forward Deployed Engineer

Updated October 25, 2026

Pay not postedSan Francisco, CA · New York, NYSan Francisco, CA +115mo ago

Forward Deployed Engineer embedding with client teams to design and deliver production-ready AI/ML solutions, from LLM integrations to data pipelines and cloud infrastructure.

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View posting at Eliza

At a glance

Posted base pay

Not posted

Median $190K

Typical senior FDE payMiddle 50% of 1,789 postings$160K–$220K

The work

Builds what the customer runs

Balance

Mostly building
Mixed
Mostly pre-sales or support

Project stages

Pre-sales (not covered)
Proof of concept
Implementation
Operations (not covered)

Coding: Writes production code
Builds for: External customers
Level: Senior
Role: Individual contributor

What they ask for

Experience

2+ yearssoftware engineering

Background

Software engineering

Tools

Python
TensorFlow
PyTorch
LangChain
LLM
OpenAI
Anthropic
Cohere
AWS
GCP
Azure
MLflow

Less common in FDE jobs

Company stage: Early12%
Company size: About 51–200 people18%
Covers proofs of concept40%
Individual contributor or manager: Individual contributor49%

Share of US FDE postings that state each fact.

The job description from Eliza

Posted by Eliza on Jul 12, 2025

About Us

We are a technology services company 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.

Job Summary

We are seeking a Forward Deployed Engineer (FDE) to work closely with our clients, translating complex business needs into scalable, production-ready AI solutions. As an FDE, you will serve as the technical face of our company on the ground—embedding with client teams, shaping solution architectures, and ensuring successful delivery. This role is perfect for engineers who love solving real-world problems, working directly with customers, and navigating the intersection of consulting and engineering.

Key Responsibilities

1. Client-Facing Solution Delivery

Partner directly with client stakeholders to understand requirements, constraints, and business objectives.
Lead the technical design and hands-on implementation of custom AI systems—including model integration, data pipelines, APIs, and deployment infrastructure.
Rapidly prototype and iterate with clients in live environments.

2. Full-Stack AI Engineering

Build and deploy ML/AI solutions using technologies like Python, TensorFlow/PyTorch, LangChain, and cloud-native tools.
Integrate LLMs and other generative models into client products and workflows.
Support model fine-tuning, prompt engineering, and evaluation pipelines where applicable.

3. Cross-Functional Collaboration

Work with internal teams (product, design, research) to shape reusable components and frameworks based on deployment experiences.
Contribute client feedback and frontline insights to improve service delivery and product strategy.

4. Technical Advisory & Enablement

Advise client technical teams on best practices for AI/ML development and deployment.
Deliver hands-on workshops, documentation, and training to enable long-term client success.
Guide clients through infrastructure and architecture decisions (e.g., cloud, security, scalability).

Qualifications

Required

2+ years of software engineering experience, ideally in full-stack or backend-focused roles.
Hands-on experience delivering real-world ML/AI projects, either independently or in collaboration with data science teams.
Strong programming skills (Python required; familiarity with JavaScript/TypeScript, Go, or similar a plus).
Comfort with modern cloud platforms (AWS, GCP, or Azure) and CI/CD workflows.
Excellent communication and client interaction skills.

Preferred

Experience with LLMs (e.g., OpenAI, Anthropic, Cohere), vector search, or prompt engineering.
Prior consulting, professional services, or customer-facing technical roles.
Familiarity with MLOps practices and tools (e.g., MLflow, Weights & Biases, SageMaker).
Knowledge of common enterprise security, data privacy, and compliance constraints.

What We Offer

Competitive compensation (salary + deployment bonuses or client uplift incentives).
Equity options in a growing AI services company.
Travel opportunities for on-site engagements (if desired).
Flexibility to work across industries and problem domains.
A collaborative, mission-driven team passionate about the real-world impact of AI.

View posting at Eliza