AI Researcher, Core ML (Turbo)

ABOUT THE ROLE

The Turbo 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.

REQUIREMENTS

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 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.

Minimum qualifications

* 3+ years of experience working on ML systems, large‑scale model training,

inference, or adjacent areas (or equivalent experience via research / open

source).

* Advanced degree in Computer Science, EE, or a related field, or equivalent

practical experience.

* Demonstrated experience owning complex technical projects end‑to‑end.

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.

RESPONSIBILITIES

* 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.

* 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.

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, 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.

COMPENSATION

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.

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.

Please see our privacy policy at https://www.together.ai/privacy

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