What are the responsibilities and job description for the Large Language Model Engineering Intern (Founding Team | Palo Alto) position at Oxmaint AI : EAM/CMMS?
Company Description
Oxmaint AI is an advanced enterprise platform designed for large-scale manufacturing and industrial facilities, offering cutting-edge solutions in Enterprise Asset Management (EAM) and Computerized Maintenance Management System (CMMS). Utilizing AI-driven intelligent operations, the platform enables predictive asset management by forecasting equipment failures, optimizing schedules, and generating automated work orders using real-time data. With seamless integration to systems like SAP, Oracle, and Microsoft Dynamics, as well as IoT connectivity, Oxmaint AI ensures scalable, efficient, and proactive maintenance across multiple sites. Empowering field technicians with advanced mobile tools, the platform supports real-time work order management, remote productivity, and compliance. Join us to revolutionize maintenance management with innovation and technology.
We are seeking a highly driven Large Language Model Engineering Intern to join our Bay Area team in Palo Alto for an initial 3-month internship, with the opportunity for extension or conversion to a full-time role based on performance and results.
This role is designed for someone passionate about building and fine-tuning large language models at scale. As the Bay Area team operates independently, the ideal candidate is a self-managed, execution-focused engineer who thrives in an environment with high autonomy and ownership.
High performers will be considered for full-time conversion with competitive compensation and stock options.
Key Responsibilities- Set up, fine-tune, and experiment with large language models (30B–120B parameter scale)
- Build and maintain fine-tuning pipelines (SFT, LoRA / QLoRA, instruction tuning)
- Optimize training and inference workflows for GPU and distributed environments
- Conduct model evaluation using benchmarks, internal datasets, and qualitative analysis
- Experiment with prompt engineering, alignment techniques, and inference optimization
- Own projects end-to-end, from data preparation to deployment
- Document experiments, findings, and best practices for internal use
- Strong interest in large language models and generative AI
- Hands-on experience with transformer-based models
- Proficiency in Python and ML tooling
- Experience with PyTorch or similar deep learning frameworks
- Ability to work independently and deliver results with minimal supervision
- Strong problem-solving and debugging skills
- Experience fine-tuning large models (30B parameters)
- Familiarity with parameter-efficient tuning (LoRA, adapters, PEFT)
- Exposure to multi-GPU or distributed training setups
- Knowledge of inference optimization (quantization, batching, KV caching)
- Prior startup, research, or open-source project experience
- Initial Term: 3 months
- Extension: Based on performance and measurable outcomes
- Full-Time Conversion: Opportunity for full-time role with stock options for high performers
- Location: Palo Alto, CA (Bay Area team operates independently)
- Deep hands-on experience with large-scale LLMs in real-world systems
- High ownership and visibility from day one
- Direct path to full-time employment and equity
- Startup-like pace with meaningful technical impact