AI​/ML Operations Engineer; MLOps Engineer

Fabley was founded on the vision that all future entertainment will be AI-generated and personalized to create the peak human experience. Today, we are building a responsible foundation for such a future.

We are a small team that wants to make an outsized impact on the world. And we would love to welcome you to join us in that effort. Passion, self-learning, and joy from working at the forefront of technology are a must. You will work in a driven team, and your input will be highly valued. You must be willing to join us in working extended hours and weekends as needed.

You will work fully remotely and must be okay with time-tracking, including screenshots.

Generative AI will change our world forever, and we need to get it right. You will work with the most cutting-edge AI technology, some of which you might introduce yourself. Fabley will live by the motto, If things are not failing, we are not innovating fast enough. Be prepared to build a solution just to have it be replaced by newly released technology.

As an AI/ML Operations Engineer, you will be responsible for hosting, inference, and fine-tuning our AI models, whether those are image generation, LLMs, TTS models, or video generation models. Your role in the company is crucial, as Fabley depends on this core technology. An excitement for new AI research and its applications is crucial for this role.

- Find ways to make the Fabley vision come to life

- Host AI models (Stable Diffusion (e.g. Civit

AI), LLM, TTS) via Cloud Computing

- Fine-tune models via user input (e.g. up- or downvotes)

- Increase the speed of inference and API responses

- Monitor and optimize cloud computing costs

- Bachelor's degree in computer science, engineering, math, or STEM discipline; OR 4+ years of professional experience in software development instead of a degree

- Developed and deployed software that has been used in real-world applications and projects

- Strong skills in debugging, performance optimization and unit testing

- Experience with any of the following: machine learning, training and fine-tuning AI models, running inference on cloud GPUs (Lambda Labs or similar)

Your base salary will be determined on a case-by-case basis and may vary based on the following considerations: job-related knowledge and skills, education, location, and experience.