What's the salary range?

Together AI lists $58–$70 for this role.

What's the tech stack?

Joblaze extracted these technologies from the posting: Deep Learning, GPU, Machine Learning, knowledge distillation, reinforcement learning.

What seniority level is this role?

Together AI targets intern candidates for this position.

Research Intern, Frontier Speculative Decoding (Summer 2027) at Together AI

Joblaze summary

The Research Intern in Frontier Speculative Decoding at Together AI focuses on exploring and developing new algorithms and architectures for draft models that enhance the efficiency of large language models. Key skills include a solid foundation in machine learning, experience with distributed training frameworks, and familiarity with modern model architectures. This role is ideal for graduate students or early-career researchers with a background in computer science or electrical engineering, particularly those with prior research experience in related fields. Together AI's internship program offers a collaborative environment where interns can engage with leading engineers and researchers.

Joblaze insights

Listed today — first seen on Joblaze October 17, 2026. Last confirmed on Together AI's careers page October 17, 2026.
Machine Learning appears in 38.5% of 104 comparable intern ai/ml roles in United States; GPU appears in 1% of 104 comparable intern ai/ml roles in United States.

Quick facts

What's the salary range?: Together AI lists $58–$70 for this role.
What's the tech stack?: Joblaze extracted these technologies from the posting: Deep Learning, GPU, Machine Learning, knowledge distillation, reinforcement learning.
What seniority level is this role?: Together AI targets intern candidates for this position.
Is this full-time or contract?: Internship for this Research Intern, Frontier Speculative Decoding (Summer 2027) role at Together AI.

From the original posting

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

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 May 17th to August 6th or June 14th to September 3rd.

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.

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Standard company text repeated across Together AI's postings is omitted here.