Machine Learning Research Engineer

What you'll do

• Turn client workflows and model shortcomings into research questions, evaluations, and experiments; create datasets, reward functions, and automated checkers.
• Run and analyze fine-tuning, preference-based, and reinforcement learning experiments, using ablations and held-out testing to evaluate changes.
• Lead research projects through deployment or publication and collaborate with researchers, infrastructure engineers, and clients.

What they ask for

• Practical experience adapting language models after pretraining and accountability for experiment outcomes.
• Evidence that modeling work reached real-world use or outside review, such as a shipped product, widely adopted open model, or accepted paper.
• Published research or open-source machine learning contributions in this area; prior experience at an organization operating at the leading edge of AI.
• Strong Python and PyTorch skills, including independently writing and debugging training code; analytical judgment and ability to work through ambiguity.

Nice to have

• Experience mentoring junior engineers or researchers.
• Master's or PhD in Computer Science; background in long-horizon reinforcement learning, model compression, generated training data, reward design, or large-scale distributed training.

Pay

USD 200,000 to 350,000 per year (base salary)

Tools

Python, PyTorch

AI focus

Applied language-model research involving post-training, fine-tuning, preference-based and reinforcement learning, model evaluation, and deployment.