Preference Model is building the next generation of training data to power the future of AI. Backed by a16z and South Park Commons.
About the role
Frontier research moves only as fast as its infrastructure permits. Building solid infrastructure is foundational to our mission of pushing self-directed learning as far as it can go. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Strong software engineering fundamentals and hands-on experience building production-grade LLM inference and training infrastructure (ideally from the ground up)
- Experience building LLM training/inference internals such as transformers, distributed training, and working on inference libraries like vLLM, SGLang, Megatron
- Experience working on RL training frameworks like Slime, veRL, Ray Train, SkyRL, Miles
- Significant experience and understanding of distributed systems principles, and have hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes), building systems for high-throughput, low-latency workloads
- Have experience with data engineering tools and building robust, scalable data pipelines
- Proficiency in core ML frameworks such as PyTorch or JAX
More about this role
Preference Model is a superintelligence data research company. We build RL environments for training capable, better-aligned superintelligences.
Almost every aspect of how a model behaves is shaped by the reward signals it's trained on. The biggest problem with AIs today is that they don't always do what we intend. Sometimes that's because they aren't capable enough, and sometimes it's because they aren't aligned. Both problems come down to the training objectives.
We're trying to address both of these issues in the most direct, highest leverage way available to us, which today means researching how to build RL environments better, not just for the AIs of today but for the AIs as they will be when they outsmart us.
Over the past year, we've built RL environments for several frontier labs, and we're backed by $16M in seed funding led by a16z. Our founding team has previous experience on Anthropic’s data team building data infrastructure, and datasets behind Claude.
Frontier research moves only as fast as its infrastructure permits. Building solid infrastructure is foundational to our mission of pushing self-directed learning as far as it can go.
We are looking for Senior ML...
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