Research Intern - Frontier Speculative Decoding

Together AI; San Francisco (CA); On-site; USD 80,000 - 96,000; Full time

Benefits offered by this job

Housing stipend

Competitive compensation

Job summary

Together AI, the AI Native Cloud, is seeking a research intern to work on speculative decoding for frontier language models. You will train and evaluate draft models at scale, explore new architectures, and validate ideas with realistic workloads on dedicated GPU clusters.

As an intern, you will contribute to open questions in draft models, distillation strategies, and verification schemes while communicating progress to the team and documenting results for wider dissemination.

Qualifications

• Strong knowledge of ML and DL fundamentals.
• Hands-on experience training or fine-tuning large models with distributed training frameworks.
• Familiarity with Mixture-of-Experts and hybrid attention architectures.

Responsibilities

• Research and prototype new speculative decoding algorithms and draft model architectures.
• Train, fine-tune and distill draft models and design rigorous experiments.
• Implement optimized serving engine versions for real-world workloads.
• Communicate plans, progress and results to the broader team.
• Share findings via technical write-ups and blog posts and help productionize ideas.

Skills

Machine Learning fundamentals

Deep Learning

Distributed training

Education

Master's or PhD in CS/EE or related

Job description

About the Role

The Speculative Decoding team at Together AI builds the draft models and algorithms that let large language models generate faster without changing their outputs. Our speculators run in production on frontier open models such as DeepSeek, Kimi and GLM. Our work sits at the intersection of model training, inference algorithms and serving systems, and we co-design all three to lower the cost and latency of generation.

As a research intern, you will dive into open questions in speculative decoding: new drafter architectures and verification schemes, knowledge distillation and training recipes that align draft and target models, and drafters that adapt as traffic shifts. You will train and fine-tune draft models at scale on dedicated GPU clusters, and test your ideas against realistic serving workloads, including long-context and agentic traffic.

Projects aim to answer important research questions in speculative decoding, enabling faster generation on frontier models, drafters that keep pace with changing workloads, and robust, reproducible evaluation under real traffic.

Past Research

Speculative decoding research by Together researchers, interns and academic collaborators includes:

• Speculative Speculative Decoding (ICLR 2026)


• When RL Meets Adaptive Speculative Training: A Unified Training-Serving System (ICML 2026)


• Beat the Long Tail: Distribution-Aware Speculative Decoding for RL Training (MLSys 2026)

Responsibilities

• Research and prototype new efficient speculative decoding algorithms and draft model architectures


• Train, fine-tune and distill draft models, and design rigorous experiments to validate hypotheses.


• Implement optimized version for serving engines to show efficacy in real world workloads


• Communicate the plans, progress, and results of projects to the broader team


• Share findings through technical write-ups and blog posts, and help bring successful ideas into production

Requirements

• Currently pursuing a Master's or Ph.D. degree in Computer Science, Electrical Engineering, or a related field


• Strong knowledge of Machine Learning and Deep Learning fundamentals


• Hands-on experience training or fine-tuning large models with distributed training frameworks (e.g., Megatron-LM, FSDP)


• Familiarity with modern model architectures, such as Mixture-of-Experts and hybrid attention

Preferred Qualifications

• Prior research experience in speculative decoding, efficient inference, or efficient machine learning


• Publications at leading conferences in machine learning or systems (e.g., NeurIPS, ICML, ICLR, MLSys)


• Experience with knowledge distillation, post-training, or reinforcement learning


• Familiarity with LLM serving engines (e.g., vLLM, SGLang, TensorRT-LLM)


• Contributions to open-source machine learning projects

About Together AI

Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month.

Internship Program Details

Our internship program runs 12 to 14 weeks, giving you the opportunity to work alongside industry-leading engineers and researchers across multiple teams. This cohort's internship dates span January 4th to April 9th.

Compensation

We offer competitive compensation, housing stipends, and other competitive benefits. The estimated US hourly rate for this role is $58 - $70 an hour. Our hourly rates are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.

For more information about our privacy policy, please visit: https://www.together.ai/privacy