Research Intern, Inference (Winter 2027)

3 нед. назад

СШАTrainee

distributed systemscompiler optimizationlarge-scale serving architecturesexperimental design

Research intern focusing on distributed inference and optimization for large foundation AI models.

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.

The Inference Research team is dedicated to building the next generation of efficient, scalable, and reliable serving systems for large foundation models, directly contributing to the mission of advancing open and transparent AI. Our work operates at the critical intersection of cutting-edge model architectures, high-performance systems engineering, and deep hardware optimization. We focus on co-designing software, algorithms, and models to significantly lower the cost and latency of modern AI systems.
As a research intern, you will dive into the complexities of distributed inference, compiler-aware optimization, and novel inference-time computation strategies (such as speculative decoding and phase-aware execution). You will be tasked with co-designing and implementing cross-layer optimizations across models, systems, and hardware, with a focus on areas like KV cache design and large-scale serving architectures.
Projects aim to unlock unprecedented performance and scale for foundation models, enabling faster serving, larger model deployment (e.g., Mixture-of-Experts), and robust, reproducible evaluation under realistic serving workloads.
Design and conduct rigorous experiments to validate hypotheses
Communicate the plans, progress, and results of projects to the broader team
Document findings in scientific publications and blog posts

Currently pursuing a final year of Bachelor's, Master's, or Ph.D. degree in Computer Science, Electrical Engineering, or a related field
Strong knowledge of Machine Learning and Deep Learning fundamentals
Experience with deep learning frameworks (PyTorch, JAX, etc.)
Strong programming skills in Python
Familiarity with Transformer architectures and recent developments in foundation models

Prior research experience in foundation models , efficient machine learning , or ML systems .
Publications at leading conferences in machine learning or systems (i.e., MLSys, ICLR ).
Experience with CUDA programming (for kernel development)
Understanding of model optimization techniques and hardware acceleration approaches
Contributions to open-source machine learning projects

We offer competitive compensation, housing stipends, and other competitive benefits. The estimated US hourly rate for this role is $58 to $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.

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.