Machine Learning Researcher
Inference is a distributed GPU network for running AI models efficiently across a global infrastructure.
Maintainer signals as of 9/2/2026
Funding history
About Inference
Inference is a distributed GPU network for running AI models. Users can connect their devices to contribute computing power, access APIs to perform model inference, and monitor workloads through a dashboard. The platform offers an open infrastructure that helps scale machine learning workloads without relying on centralized servers.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
Conduct research into experimental models, training systems, and modalities to create novel products for customers. Explore new architectures and learning methods, optimize latency and efficiency, run rigorous experiments, train customer models, and validate research through production evaluations.
Requirements
- 3+ years of experience training AI models using PyTorch
- Deep understanding of transformer architectures, attention mechanisms, and model internals
- Hands-on experience with post-training LLMs using SFT, RLHF, DPO, or other alignment techniques
- Experience with LLM-specific training frameworks such as Hugging Face Transformers, DeepSpeed, Megatron, TRL, or similar
- Strong experimental methodology, including the ability to design, run, and analyze rigorous experiments
- Track record of implementing ideas from recent ML papers
- Experience training on NVIDIA GPUs at scale
- Strong foundation in ML fundamentals including optimization, loss functions, regularization, and generalization
Responsibilities
- Research and experiment with new model architectures to improve quality, efficiency, or capability
- Explore methods to decrease inference latency and improve serving efficiency
- Run experiments with new learning methods, including novel approaches to SFT, RLHF, DPO, and other post-training techniques
- Perform reinforcement learning research to improve model alignment and capability
- Develop and improve the distillation pipeline for training high-quality models from frontier teachers
- Train models for clients and run evaluations to validate research findings in production settings
- Create robust benchmarks and evaluation frameworks to ensure custom models match or exceed frontier performance
- Stay current with ML research and identify techniques that can improve the platform
- Collaborate with applied engineers to bring successful research into production systems
- Document findings and share knowledge with the team
Benefits
- Equity
- Comprehensive benefits