Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading AI Developer Cloud platform enables more than one million developers, ranging from independent researchers to teams operating frontier models in production, to experiment, train, fine-tune, deploy, and scale AI workloads on a single platform. The platform has processed more than 20 billion inference requests and recently secured $100 million in Series A funding.
The organization operates as a small, remote-first team that emphasizes ownership, speed, technical excellence, and delivering products that serve developers at significant scale. This position is based in the United States within the PST timezone.
As part of the Revenue Team, the Forward Deployed Engineer will work closely with customers as well as sales, product, engineering, support, supply, and infrastructure teams to deliver a seamless customer experience. The role will focus on providing technical guidance, resolving complex challenges, supporting customer onboarding, and building strong customer relationships.
The successful candidate will help support the continued growth of the cloud platform by addressing technical challenges, improving customer operations, and collaborating across teams. The role requires a combination of strong technical expertise, problem-solving ability, and excellent communication skills.
Key Responsibilities
- Participate in customer sales meetings, explain platform technologies, provide architectural recommendations, and develop proof-of-concept solutions to support onboarding for high-spending customers.
- Troubleshoot and resolve critical or complex customer issues involving configuration, performance, functionality, compatibility, and code errors.
- Use code analysis, scripting, testing, log analysis, and remote-access tools to identify root causes and develop effective solutions or workarounds.
- Communicate effectively with customers and internal teams, including engineering, sales, supply, and product management.
- Assist support teams with escalated technical tickets and collaborate with engineering teams on workarounds and bug fixes.
- Work with infrastructure teams to investigate and resolve GPU server-related issues.
- Relay customer and support feedback to engineering teams to help influence product development and improvements.
- Contribute to product development and testing initiatives based on customer requirements and technical insights.
- Create and maintain technical documentation, including knowledge base articles, FAQs, guides, and manuals.
- Develop and deliver technical training sessions, webinars, and product demonstrations for customers, partners, and internal teams.
Requirements
- Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, Information Technology, or a related field, or equivalent professional experience.
- 3+ years of professional software development experience.
- Strong problem-solving skills and the ability to work effectively in a collaborative environment.
- Familiarity with applied AI use cases, including inference, fine-tuning, LLM-based applications, or agentic systems.
- Excellent communication skills and strong attention to detail.
- Must be located in the United States within the PST region.
- Successful completion of a background check.
Preferred Qualifications
- Strong technical skills with proficiency in Python, including Django, Flask, and PyTorch; JavaScript, including React and Node.js; and Go.
- Hands-on experience with full-stack software development.
- Knowledge of Linux/Ubuntu operating systems and containerization technologies such as Docker.
- Experience with SQL databases such as MySQL and PostgreSQL and NoSQL databases such as MongoDB.
- Understanding of data center components, server hardware, network operations, and common networking protocols including TCP/IP, HTTP/HTTPS, SSH, and DNS.
- Experience deploying and managing AI/ML models as services or APIs.
- Strong understanding of machine learning models, including training, inference, and deployment processes on AI cloud platforms.
- Ability to write and execute scripts, queries, and commands.
- Ability to read and understand source code, logs, errors, and technical traces.
Disclaimer
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