Member of Technical Staff, Training Technical Staff · San Francisco, CA · Full time · On-site

Belvedir; San Francisco (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

Belvedir (Fractal Machine Research, Inc.) is seeking a hands-on ML engineer to design and run training experiments across SFT, distillation, RL, and RLHF, working directly with customer teams to understand objectives and deliver practical results.

You will build pipelines to collect, clean, and synthesize data, create robust evaluations, and turn wins into repeatable training recipes and tools for the team, while collaborating closely with founders.

Qualifications

• Experience with LLM training: post-training, RL, RLHF, evals, or synthetic data.
• You have trained or fine-tuned models on real, messy data.
• Know what makes an eval trustworthy, and how models game one that is not.
• You can work directly with customers and translate what you hear into what to train next.
• Founders or early-engineer experience is a plus.

Responsibilities

• Design and run training experiments: SFT, distillation, RL on customer tasks.
• Build pipelines that turn raw customer data into high-quality training sets: collection, cleaning, filtering, and synthesis.
• Build task-specific evaluations and use them to drive decisions about data and training recipes.
• Turn one-off customer wins into repeatable training recipes and tooling the whole team can use.
• Work directly with customers and founders to decide what to train next.

Skills

LLM training

SFT

Distillation

RLHF

Evaluation design

Data cleaning & synthesis

Customer collaboration

End-to-end ownership

Job description

Belvedir's mission is to make the creation and deployment of AI models accessible to everyone. To achieve this we build autonomous systems for cleaning raw data, creating benchmarks, asynchronous RL, test-time training, inference optimization, and much more.

We work with startups, enterprises, and some of the world's leading applied AI companies. We have raised a seed round from an amazing group of notable VCs and angels.

About the role

• Design and run training experiments: SFT, distillation, preference tuning, and RL on customer tasks.
• Build the pipelines that turn raw customer data into high-quality training sets: collection, cleaning, filtering, and synthesis.
• Build task-specific evaluations and use them to drive decisions about data and training recipes.
• Turn one-off customer wins into repeatable training recipes and tooling the whole team can use.
• Work directly with customers and founders to decide what to train next.

What we are looking for

• Experience with LLM training: post-training, RL, RLHF, evals, or synthetic data, in industry or research.
• You have trained or fine-tuned models on real, messy data.
• You know what makes an eval trustworthy, and how models game one that is not.
• You can work directly with customers and turn what you hear into what to train next.
• You own problems end to end and ship. Founder or early-engineer experience is a plus.