Machine Learning Researcher

Help us push the boundaries of what's possible in LLM post-training. If you love training models, exploring new architectures, running experiments, and turning research insights into products that ship, we'd love to meet you.

You will be responsible for conducting research into experimental models, training systems, and modalities to create novel products for our customers. Your work will span from exploring new architectures and learning methods to optimizing latency and efficiency, with the goal of delivering better models to customers.

Your north star is pushing the frontier of what's possible in LLM post-training. You'll explore new techniques, run rigorous experiments, and when something works, help bring it into production with the help of your teammates. This includes training models for customers and running evaluations as part of validating your research. This role reports directly to the founding team. You'll have the autonomy, a large compute budget / GPU reservation, and technical support to explore ambitious ideas and ship the ones that work.

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  Research and experiment with new model architectures to improve quality, efficiency, or capability

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  Explore methods to decrease inference latency and improve serving efficiency

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  Run experiments with new learning methods, including novel approaches to SFT, RLHF, DPO, and other post-training techniques

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  Perform reinforcement learning research to improve model alignment and capability

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  Develop and improve our distillation pipeline for training high-quality models from frontier teachers

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  Train models for clients and run evaluations to validate research findings in production settings

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  Create robust benchmarks and evaluation frameworks that ensure custom models match or exceed frontier performance

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  Stay current with ML research and identify techniques that can improve our platform

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  Collaborate with applied engineers to bring successful research into production systems

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  Document findings and share knowledge with the team

• 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 (e.g., Hugging Face Transformers, DeepSpeed, Megatron, TRL, or similar)

• Strong experimental methodology, including 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: optimization, loss functions, regularization, generalization

• Publications in ML venues

• Experience with model distillation or knowledge transfer

• Experience with LLM speed optimization techniques

• Familiarity with vision encoders, multimodal models, or other modalities

• Experience with distributed training and infrastructure at scale

• Contributions to open-source ML projects

You don't need to tick every box. Curiosity and the ability to learn quickly matter more.

Inference.net is an equal opportunity employer. We welcome applicants from all backgrounds and don't discriminate based on race, color, religion, gender, sexual orientation, national origin, genetics, disability, age, or veteran status.

If you're excited about pushing the boundaries of custom AI research, we'd love to hear from you. Please send your resume and GitHub to amar@inference.net and/or here on Ashby.