Machine Learning Researcher
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Before you apply
Salary$250k – $350k
LocationSan Francisco
Work modeon-site
Typefull-time
SenioritySenior
Experience3+ yrs
DepartmentEngineering
EquityIncluded
Company size5+ people
PostedJan 5, 2026 · 10mo ago
Verified live16h ago
May no longer be actively hiring
Posted by the company on Jan 5, 2026100% of this company's open roles have been listed for 90+ days
At a glanceSummarised by Seekless from the posting.
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
Skills
PyTorch
Hugging Face Transformers
DeepSpeed
Megatron
TRL
NVIDIA GPUs
Benefits
Equity
About the role
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.
About the company
About Inference.net
Inference.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.
We 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.
About the Role
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.
Key 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
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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
•
Stay current with ML research and identify techniques that can improve our platform
•
Collaborate with applied engineers to bring successful research into production systems
•
Document findings and share knowledge with the team
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 (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
Nice-to-Have
•
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.
Compensation
We 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.
Equal Opportunity
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.
Hiring process
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 [email protected] and/or here on Ashby.