7mo ago

Forward Deployed Engineer - Tech

United States·San Franciscofull-timemid

OtherSoftware EngineerForward Deployed EngineerSoftware Engineering

0 views0 saves0 applied

Apply Now

Quick Summary

Overview

Overview We are looking for a Forward Deployed Engineer – Tech to work at the intersection of AI engineering, product deployment, and client engagement.

Technical Tools

OtherSoftware EngineerForward Deployed EngineerSoftware Engineering

We are looking for a Forward Deployed Engineer – Tech to work at the intersection of AI engineering, product deployment, and client engagement. This role partners closely with customers and internal engineering teams to build, deploy, and operationalize AI-powered applications in real-world commercial environments.

You will combine software engineering, AI application development, and product thinking to translate complex business workflows into scalable AI systems. The role requires hands-on experience building AI-enabled applications, integrating LLMs, and deploying production-grade infrastructure, along with the ability to work directly with clients to implement solutions.

This position is ideal for engineers who enjoy building quickly, solving ambiguous problems, working directly with customers, and shipping production systems that power real-world AI applications.

Responsibilities

~1 min read

Build AI-powered applications using LLMs, APIs, and modern AI frameworks.
Design and implement workflows that combine structured enterprise data with generative AI capabilities.
Develop prototypes and production-ready systems that support real-world client deployments.

Design and manage infrastructure required to deploy AI applications reliably and securely.
Work with cloud environments to support model hosting, data pipelines, and scalable application deployment.
Implement monitoring, performance optimization, and system reliability practices.

Work directly with clients and internal teams to implement and customize AI-powered applications for commercial and operational use cases.
Translate client workflows and requirements into scalable AI-driven software solutions.
Serve as the technical bridge between client teams, product, and engineering.

Partner with product and engineering teams to translate client needs into scalable platform capabilities.
Contribute to the development of reusable infrastructure and components for AI application deployment.
Support rapid prototyping and experimentation to accelerate product development.

Work closely with client technical teams to integrate AI solutions into their environments.
Explain technical architectures and deployment approaches to both technical and non-technical stakeholders.
Collaborate cross-functionally across product, engineering, and founders directly.

Bachelor’s or Master’s degree in Computer Science, Engineering, AI/ML, or a related technical field.

0–3 years of experience in software engineering, AI engineering, or machine learning systems development.
Experience building applications using Python, JavaScript, or similar languages.
Experience working with LLM APIs, AI frameworks, or generative AI tooling is preferred.
Familiarity with cloud infrastructure and application deployment is preferred.

Experience building and deploying production software systems.
Familiarity with AI application development frameworks, APIs, and model integration.
Understanding of software architecture, system design, and backend services.
Experience with containerization, APIs, or cloud platforms is a plus.

Exposure to pharmaceutical or healthcare technology environments is a plus but not required.
Interest in applying AI systems to life sciences commercial and operational workflows is preferred.

Strong engineering and problem-solving abilities.
Ability to build and iterate quickly in ambiguous environments.
Comfort working in client-facing technical roles.
Strong collaboration across engineering, product, and customer teams.

Work directly with clients to deploy real AI systems into production environments.
Build AI-powered applications rather than just models or prototypes.
Blend software engineering, AI infrastructure, and customer-facing problem solving.
Help build the next generation of AI-native platforms for life sciences.