Applied Machine Learning Engineer

Vidman AI; United States; Remote; USD 150,000 - 230,000; Full time

Job summary

Vidman AI is seeking an ML engineer to help customers fine-tune open language and vision-language models, from data inspection to evaluating and serving trained weights. You will lead fine-tuning work, build tools for dataset checks and training orchestration, and troubleshoot issues like unstable loss and memory limits, while translating customer needs into product improvements.

The role blends hands-on ML with building internal tooling to make training workflows reliable and scalable, with

Qualifications

• Five+ years of ML production experience.
• Experience fine-tuning LLMs or vision-language models and interpreting training curves.
• Strong Python and PyTorch skills; manage GPU memory.
• Data-first approach; inspect datasets before selecting models.
• Clear communication to explain failed runs to customers.

Responsibilities

• Lead fine-tuning work from dataset review and method choice through run configuration, evaluation, and handoff.
• Build tools for dataset checks, training orchestration, and model evaluation across a large open-model catalog.
• Investigate training and deployment issues, including unstable loss, adapter behavior, tokenization edge cases, and GPU memory limits.
• Identify recurring customer needs and propose prioritized improvements to product and research teams.
• Help customers deploy fine-tuned model weights through the inference stack.

Skills

Python

PyTorch

Data inspection

Communication

Job description

Role overview

Help customers fine-tune open language and vision-language models, from inspecting their data through evaluating and serving trained weights. The work combines hands‑on ML problem solving with building internal tools that make training workflows more reliable and turn recurring customer needs into platform improvements.

Responsibilities

• Lead fine-tuning work from dataset review and method choice through run configuration, evaluation, and handoff.
• Build tools for dataset checks, training orchestration, and model evaluation across a large open-model catalog.
• Investigate training and deployment issues, including unstable loss, adapter behavior, tokenization edge cases, and GPU memory limits.
• Identify recurring customer needs and propose prioritized improvements to product and research teams.
• Help customers deploy fine-tuned model weights through the inference stack.

Requirements

• At least five years applying machine learning in production settings.
• Practical experience fine-tuning LLMs or vision-language models, including configuring runs and interpreting training curves.
• Strong Python and PyTorch skills, with the ability to reason about constrained GPU memory.
• A data-first approach that includes inspecting datasets before selecting or adjusting models.
• Clear communication, including explaining failed runs and their causes to customers.