Together AI에서 Research Engineer를 채용합니다. LLM 추론 엔진 최적화 및 RL/post-training 파이프라인 구축을 담당하며, Python 기반의 고성능 시스템 구현 역량이 필수입니다. ML 시스템 또는 RL 분야에서 3년 이상의 경력을 보유한 분을 찾습니다. 연구 아이디어를 실제 프로덕션 환경으로 구현하는 데 열정이 있는 분들의 많은 지원 바랍니다.
This is a research engineering role with direct production impact. You won’t be publishing ideas in isolation—you will translate new RL algorithms, scheduling methods, and inference optimizations into production-grade systems that power Together’s API. Success in this role means shipping measurable improvements in latency, throughput, cost, and model quality at scale. We are looking for researchers who enjoy owning systems end-to-end and turning frontier ideas into robust infrastructure.
The Core ML (Turbo) at Together AI team sits at the intersection of efficient inference (algorithms, architectures, engines) and post‑training / RL systems. We build and operate the systems behind Together’s API, including high‑performance inference and RL/post‑training engines that can run at production scale.
Our mandate is to push the frontier of efficient inference and RL‑driven training: making models dramatically faster and cheaper to run, while improving their capabilities through RL‑based post‑training (e.g., GRPO‑style objectives). This work lives at the interface of algorithms and systems: asynchronous RL, rollout collection, scheduling, and batching all interact with engine design, creating many knobs to tune across the RL algorithm, training loop, and inference stack. Much of the job is modifying production inference systems—for example, SGLang‑ or vLLM‑style serving stacks and speculative decoding systems such as ATLAS—grounded in a strong understanding of post‑training and inference theory, rather than purely theoretical algorithm design.
You’ll work across the stack—from RL algorithms and training engines to kernels and serving systems—to build and improve frontier models via RL pipelines. People on this team are often spiky: some are more RL‑first, some are more systems‑first. Depth in one of these areas plus appetite to collaborate across (and grow toward more full‑stack ownership over time) is ideal.
Advance inference efficiency end‑to‑end
Design and prototype algorithms, architectures, and scheduling strategies for low‑latency, high‑throughput inference.
Implement and maintain changes in high‑performance inference engines (e.g., SGLang‑ or vLLM‑style systems and Together’s inference stack), including kernel backends, speculative decoding (e.g., ATLAS), quantization, etc.
Profile and optimize performance across GPU, networking, and memory layers to improve latency, throughput, and cost.
Unify inference with RL / post‑training
Design and operate RL and post‑training pipelines (e.g., RLHF, RLAIF, GRPO, DPO‑style methods, reward modeling) where 90+% of the cost is inference, jointly optimizing algorithms and systems.
Make RL and post‑training workloads more efficient with inference‑aware training loops—for example, async RL rollouts, speculative decoding, and other techniques that make large‑scale rollout collection and evaluation cheaper.
Use these pipelines to train, evaluate, and iterate on frontier models on top of our inference stack.
Co‑design algorithms and infrastructure so that objectives, rollout collection, and evaluation are tightly coupled to efficient inference, and quickly identify bottlenecks across the training engine, inference engine, data pipeline, and user‑facing layers.
Run ablations and scale‑up experiments to understand trade‑offs between model quality, latency, throughput, and cost, and feed these insights back into model, RL, and system design.
Own critical systems at production scale
Profile, debug, and optimize inference and post-training services under real production workloads, taking research ideas all the way to stable, measurable improvements in deployed systems.
Drive roadmap items that require real engine modification—changing kernels, memory layouts, scheduling logic, and APIs as needed.
Establish metrics, benchmarks, and experimentation frameworks to validate improvements rigorously.
Provide technical leadership (Staff level)
Set technical direction for cross‑team efforts at the intersection of inference, RL, and post‑training.
Mentor other engineers and researchers on full‑stack ML systems work and performance engineering.
We don’t expect anyone to check every box below. People on this team typically have deep expertise in one or more areas and enough breadth (or interest) to work effectively across the stack. The closer you are to full‑stack (inference + post‑training/RL + systems), the stronger the fit—but being spiky in one area and eager to grow is absolutely okay.
You might be a good fit if you:
Have a bias toward implementation and shipping—you are excited to modify real engines and services, not just prototype in research code.
Have strong expertise in at least one of the following, and are excited to collaborate across (and grow into) the others:
Systems‑first profile: Large‑scale inference systems (e.g., SGLang, vLLM, FasterTransformer, TensorRT, custom engines, or similar), GPU performance, distributed serving.
RL‑first profile: RL / post‑training for LLMs or large models (e.g., GRPO, RLHF/RLAIF, DPO‑like methods, reward modeling), and using these to train or fine‑tune real models.
Model architecture design for Transformers or other large neural nets.
Distributed systems / high‑performance computing for ML.
Are comfortable working from algorithms to engines:
Strong coding ability in Python
Experience profiling and optimizing performance across GPU, networking, and memory layers.
Able to take a new sampling method, scheduler, or RL update and turn it into a production‑grade implementation in the engine and/or training stack.
Have a solid research foundation in your area(s) of depth:
Track record of impactful work in ML systems, RL, or large‑scale model training (papers, open‑source projects, or production systems).
Can read new RL / post‑training papers, understand their implications on the stack, and design minimal, correct changes in the right layer (training engine vs. inference engine vs. data / API).
Operate well as a full‑stack problem solver:
You naturally ask: “Where in the stack is this really bottlenecked?”
You enjoy collaborating with infra, research, and product teams, and you care about both scientific quality and user‑visible wins.
If you’re excited about the role and strong in some of these areas, we encourage you to apply even if you don’t meet every single requirement.
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, hardware, 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 advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure.
We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $200,000 - $280,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.
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.
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