ML Engineer

About the Role

As a Machine Learning Engineer, you’ll push the frontier of post-training for large language models from supervised fine-tuning to advanced reinforcement learning to train powerful agents capable of complex, end-to-end reasoning and action.

You’ll work side-by-side with our researchers to rapidly iterate on alignment strategies, implement state-of-the-art training algorithms, and run large-scale experiments that directly improve our production models.

If you love making models smarter, safer, and more capable, and want to work at the cutting edge of LLM agent training, this role is for you.

Responsibilities

• Post-train SOTA LLMs on query answering by using the latest in supervised and reinforcement learning techniques

• Own the full stack data, training, and eval pipelines required to post-train LLM models

• Deliver Sonar models that provide SOTA query answering

• Work closing with engineering teams to integrate Sonar models into our product

Qualifications

• Proven experience with large-scale LLMs and Deep Learning systems

• Strong programming skills; versatility is a plus

• Self-starter with a willingness to take ownership of tasks

• Passion for tackling challenging problems

Bonus

• Experience training agentic LLMs with end-to-end reinforcement learning

• Prior research experience

What makes us interesting

• Small, elite team of ex-founders, researchers from top AI Labs, top CS grads, and engineers from top companies

• True ownership You will not be blocked by bureaucracy, shipping meaningful work within weeks rather than months

• Serious momentum We're well-funded by top investors, moving fast, and focused on execution

What we do

• Ship consumer products powered by cutting-edge AI research, and

• Build infrastructure that facilitates research and product, and

• Innovate cutting-edge research that will open up new consumer product forms

The Details

• Full-time, onsite role in Menlo Park

• Startup hours apply

• Generous salary, with additional benefits to be discussed during the hiring process