Applied Machine Learning Engineer

What you'll do

• Own training projects from data intake and preparation through model training, evaluation, and delivery.
• Build data pipelines, dashboards, benchmarks, and automation for training and model evaluation.
• Apply SFT, RL, and model optimization techniques; bring research features into production and collaborate on scaling GPU training.

What they ask for

• 2+ years of experience training AI models using PyTorch.
• Hands-on experience post-training LLMs with SFT or RL, and a strong understanding of transformer architectures and training.
• Experience with LLM training frameworks, training on NVIDIA GPUs, and building ETL pipelines for large datasets.
• Track record creating benchmarks and evaluations, and applying research techniques to production systems.

Nice to have

• Experience with model distillation or knowledge transfer.
• Experience building dashboards and data visualization tools; familiarity with vision encoders, multimodal models, distributed training at scale, or open-source ML contributions.

Pay

$220,000 - $320,000 USD base salary

Tools

PyTorch, Hugging Face Transformers, DeepSpeed, Axolotl, NVIDIA GPUs, SFT, RL

AI focus

Train, post-train, evaluate, and optimize specialized language models, including applying SFT and RL and improving model quality and efficiency.