{"schemaVersion":"jobsearcher.job.v1","id":"cee27a95e6255e4af8f253c1","url":"https://jobsearcher.com/jobs/cee27a95e6255e4af8f253c1","canonicalUrl":"https://jobsearcher.com/jobs/cee27a95e6255e4af8f253c1","title":"Machine Learning Researcher","description":"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.\nAbout Inference.net\nInference.net trains and hosts specialized language models for companies who want frontier-quality AI at a fraction of the cost. The models we train match GPT-5 accuracy but are smaller, faster, and up to 90% cheaper. Our platform handles everything end-to-end: distillation, training, evaluation, and planet-scale hosting.\nWe are a well-funded ten-person team of engineers who work in-person in downtown San Francisco on difficult, high-impact engineering problems. Everyone on the team has been writing code for over 10 years, and has founded and run their own software companies. We are high-agency, adaptable, and collaborative. We value creativity alongside technical prowess and humility. We work hard, and deeply enjoy the work that we do. Most of us are in the office 4 days a week in SF; hybrid works for Bay Area candidates.\nAbout the Role\nYou 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.\nYour 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.\nKey Responsibilities\nResearch and experiment with new model architectures to improve quality, efficiency, or capability\nExplore methods to decrease inference latency and improve serving efficiency\nRun experiments with new learning methods, including novel approaches to SFT, RLHF, DPO, and other post-training techniques\nPerform reinforcement learning research to improve model alignment and capability\nDevelop and improve our distillation pipeline for training high-quality models from frontier teachers\nTrain models for clients and run evaluations to validate research findings in production settings\nCreate robust benchmarks and evaluation frameworks that ensure custom models match or exceed frontier performance\nStay current with ML research and identify techniques that can improve our platform\nCollaborate with applied engineers to bring successful research into production systems\nDocument findings and share knowledge with the team\nRequirements\n3+ years of experience training AI models using PyTorch\nDeep understanding of transformer architectures, attention mechanisms, and model internals\nHands-on experience with post-training LLMs using SFT, RLHF, DPO, or other alignment techniques\nExperience with LLM-specific training frameworks (e.g., Hugging Face Transformers, DeepSpeed, Megatron, TRL, or similar)\nStrong experimental methodology, including ability to design, run, and analyze rigorous experiments\nTrack record of implementing ideas from recent ML papers\nExperience training on NVIDIA GPUs at scale\nStrong foundation in ML fundamentals: optimization, loss functions, regularization, generalization\nNice-to-Have\nPublications in ML venues\nExperience with model distillation or knowledge transfer\nExperience with LLM speed optimization techniques\nFamiliarity with vision encoders, multimodal models, or other modalities\nExperience with distributed training and infrastructure at scale\nContributions to open-source ML projects\nYou don't need to tick every box. Curiosity and the ability to learn quickly matter more.\nCompensation\nWe offer competitive compensation, equity in a high-growth startup, and comprehensive benefits. The base salary range for this role is $250,000 - $350,000, plus equity and benefits, depending on experience.\nEqual Opportunity\nInference.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.\nIf 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.\nCompensation Range: $250K - $350K","company":"Inference","rawCompany":"inference","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-04-09T08:32:21.242Z","occupations":[{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Researcher","description":"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.\nAbout Inference.net\nInference.net trains and hosts specialized language models for companies who want frontier-quality AI at a fraction of the cost. The models we train match GPT-5 accuracy but are smaller, faster, and up to 90% cheaper. Our platform handles everything end-to-end: distillation, training, evaluation, and planet-scale hosting.\nWe are a well-funded ten-person team of engineers who work in-person in downtown San Francisco on difficult, high-impact engineering problems. Everyone on the team has been writing code for over 10 years, and has founded and run their own software companies. We are high-agency, adaptable, and collaborative. We value creativity alongside technical prowess and humility. We work hard, and deeply enjoy the work that we do. Most of us are in the office 4 days a week in SF; hybrid works for Bay Area candidates.\nAbout the Role\nYou 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.\nYour 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.\nKey Responsibilities\nResearch and experiment with new model architectures to improve quality, efficiency, or capability\nExplore methods to decrease inference latency and improve serving efficiency\nRun experiments with new learning methods, including novel approaches to SFT, RLHF, DPO, and other post-training techniques\nPerform reinforcement learning research to improve model alignment and capability\nDevelop and improve our distillation pipeline for training high-quality models from frontier teachers\nTrain models for clients and run evaluations to validate research findings in production settings\nCreate robust benchmarks and evaluation frameworks that ensure custom models match or exceed frontier performance\nStay current with ML research and identify techniques that can improve our platform\nCollaborate with applied engineers to bring successful research into production systems\nDocument findings and share knowledge with the team\nRequirements\n3+ years of experience training AI models using PyTorch\nDeep understanding of transformer architectures, attention mechanisms, and model internals\nHands-on experience with post-training LLMs using SFT, RLHF, DPO, or other alignment techniques\nExperience with LLM-specific training frameworks (e.g., Hugging Face Transformers, DeepSpeed, Megatron, TRL, or similar)\nStrong experimental methodology, including ability to design, run, and analyze rigorous experiments\nTrack record of implementing ideas from recent ML papers\nExperience training on NVIDIA GPUs at scale\nStrong foundation in ML fundamentals: optimization, loss functions, regularization, generalization\nNice-to-Have\nPublications in ML venues\nExperience with model distillation or knowledge transfer\nExperience with LLM speed optimization techniques\nFamiliarity with vision encoders, multimodal models, or other modalities\nExperience with distributed training and infrastructure at scale\nContributions to open-source ML projects\nYou don't need to tick every box. Curiosity and the ability to learn quickly matter more.\nCompensation\nWe offer competitive compensation, equity in a high-growth startup, and comprehensive benefits. The base salary range for this role is $250,000 - $350,000, plus equity and benefits, depending on experience.\nEqual Opportunity\nInference.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.\nIf 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.\nCompensation Range: $250K - $350K","datePosted":"2026-04-09T08:32:21.242Z","dateModified":"2026-04-09T08:32:21.242Z","hiringOrganization":{"@type":"Organization","name":"Inference","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"cee27a95e6255e4af8f253c1"},"url":"https://jobsearcher.com/jobs/cee27a95e6255e4af8f253c1"}}