Research Intern RL & Post-Training Systems, Turbo (Fall 2026)
San Francisco, California, United States · On-site
$58/hr–$63/hr
Entry level$534M raised
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Skills: Reinforcement Learning, Post-Training, ML Systems, Python, C++
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$150k–$200k/yr
Mid level$534M raised
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San Francisco, California, United States · On-site
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Research Intern RL & Post-Training Systems, Turbo (Fall 2026)
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$58/hr–$63/hr
Internship
postgraduate degree
Housing Stipends
Posted 28d ago
~40 hrs/week
Responsibilities
The intern will co-design RL algorithms and inference systems to improve the efficiency and scalability of post-training for large language models. Key tasks include developing inference-aware RL objectives and exploring system-level optimizations to unlock new learning capabilities.
Requirements
Candidates should be pursuing a PhD or MS in Computer Science, EE, or a related field with research experience in RL, post-training, or ML systems. Proficiency in Python is required, with a willingness to work across abstraction layers including C++ or CUDA.
Full job description
About the Role
The Turbo Research team investigates how to make post-training and reinforcement learning for large language models efficient, scalable, and reliable. Our work sits at the intersection of RL algorithms, inference systems, and large-scale experimentation, where the cost and structure of inference dominate overall training efficiency and shape what learning algorithms are practical.
As a research intern, you will study RL and post-training methods whose performance and scalability are tightly coupled to inference behavior, co-designing algorithms and systems rather than treating them independently. Projects aim to unlock new regimes of experimentation—larger models, longer rollouts, and more complex evaluations—by rethinking how inference, scheduling, and training interact.
Requirements
Pursuing a PhD or MS in Computer Science, EE, or a related field (exceptional undergraduates considered)
Have research experience in one or more of:
RL or post-training for large models (e.g., RLHF, RLAIF, GRPO, preference optimization)
ML systems (inference engines, runtimes, distributed systems)
Large-scale empirical ML research or evaluation
Are comfortable with empirical research by designing controlled experiments, while interpreting noisy results and drawing principled conclusions
Can work across abstraction layers:
Strong Python skills for experimentation
Willingness to modify inference or training systems (experience with C++, CUDA, or similar is a plus)
Example Research Directions
Intern projects are tailored to your background and interests, and may include:
Inference-Aware RL & Post-Training
Designing RL or preference-optimization objectives that explicitly account for inference cost and structure (e.g., speculative decoding, partial rollouts, controllable sampling).
Studying how inference-time approximations affect learning dynamics in GRPO-, RLHF-, RLAIF-, or DPO-style methods.
Analyzing bias, variance, and stability trade-offs introduced by accelerated inference within RL loops.
RL-Centric Inference Systems
Developing inference mechanisms that support deterministic, reproducible RL rollouts at scale.
Exploring batching, scheduling, and memory-management strategies optimized for RL and evaluation workloads rather than pure serving.
Investigating how KV-cache policies, sampling controls, or runtime abstractions influence learning efficiency.
Scaling Laws & Cost–Quality Trade-offs
Empirically characterizing how reward improvement and generalization scale with rollout cost, latency, and throughput.
Quantifying when systems-level optimizations change algorithmic behavior rather than only reducing runtime.
Identifying regimes where inference efficiency unlocks qualitatively new learning capabilities.
Evaluation & Measurement
Designing rigorous benchmarks and diagnostics for post-training and RL efficiency.
Studying failure modes in long-horizon training and how system constraints shape outcomes.
Preferred Qualifications
Publications at leading ML and NLP conferences (such as NeurIPS, ICML, ICLR, ACL, or EMNLP)
Understanding of model optimization techniques and hardware acceleration approaches
Contributions to open-source machine learning projects
Internship Program Details
Our fall internship program spans over 12 to 16 weeks where you’ll have the opportunity to work with industry-leading engineers building a cloud from the ground up and possibly contribute to influential open source projects. Our internship dates are September 14th to December 18th.
About Together AI
Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancements such as FlashAttention, Mamba, FlexGen, Petals, Mixture of Agents, and RedPajama.
Compensation
We offer competitive compensation, housing stipends, and other competitive benefits. The estimated US hourly rate for this role is $58 to $63 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.
Accelerate inference, model shaping, and pre-training on a research-optimized platform.
Industry
Software Development
Company size
201-500 employees
Founded
2022
Headquarters
San Francisco, California
LinkedIn followers
91,023
Total funding
$534M
Together AI is the AI Native Cloud, purpose-built for AI engineers and researchers with a full suite of tooling across inference, model shaping, and pre-training. AI natives can use Together AI as a full-stack AI platform — from a high- performance inference engine built for reliable and fast scaling to on-demand GPU clusters and massive-scale AI factories.
Together AI continuously pushes the frontier forward by productizing cutting-edge research from our world-leading AI systems research team. By combining research velocity with production-grade infrastructure, we enable companies to reliably scale AI-native applications as fast as the field evolves.
Trusted by leading AI natives like Cursor, Decagon, Eleven Labs, AI21, Hedra, and Cartesia, as well as SaaS innovators such as Salesforce, Zoom, and Zomato, Together AI powers the next generation of AI-native applications.
Offices: 251 Rhode Island St, Suite 205, San Francisco, California 94103, US
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