Research Engineer, Lead

What you'll do

• Run post-training research and engineering projects, from experiment design through evaluation and implementation.
• Train and evaluate reward and preference models using professional design decisions; investigate methods such as fine-tuning, distillation, preference optimization, and reinforcement learning.
• Build reliable training and evaluation infrastructure, document results, and work with product and design teams to connect model behavior to real workflows.

What they ask for

• Strong programming and software engineering skills, particularly for machine learning systems.
• Hands-on experience training or fine-tuning machine learning models.
• Experience with generative models, including diffusion or flow models, through professional work, research, or substantial technical projects.
• Familiarity with post-training methods and experience designing experiments and evaluating model performance.

Nice to have

• Experience building high-performance training or inference systems, optimizing ML workloads, or working with large-scale training infrastructure.
• Experience with preference data or human-feedback systems, taking ML experiments toward production, or interest in industrial design and physical products.

Pay

$164,000 - $215,000 USD (base salary)

Tools

diffusion, flow models, supervised fine-tuning, distillation, preference optimization, reinforcement learning

AI focus

Post-training generative models to learn professional design judgment from preference and reward signals, including training and evaluating preference/reward models and applying supervised fine-tuning, distillation, preference optimization, and reinforcement learning.