research
Posted 2 weeks agoAI Researcher, Core ML (Turbo)
at togetherai
San Francisco, United StatesOn-site
Responsibilities
- Model architecture design for Transformers or other large neural nets.
- Track record of impactful work in ML systems, RL, or large‑scale model training (papers, open‑source projects, or production systems).
- 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.
- 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.
- 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
- 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.
- Mentor other engineers and researchers on full‑stack ML systems work and performance engineering. About Together AI
Requirements
- 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.
- 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.
- People on this team typically have deep expertise in one or more areas and enough breadth (or interest) to work effectively across the stack.
- Systems‑first profile: Large‑scale inference systems (e.g., SGLang, vLLM, FasterTransformer, TensorRT, custom engines, or similar), GPU performance, distributed serving.
- Distributed systems / high‑performance computing for ML.
- 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.
- 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).
- experience working on ML systems, large‑scale model training, inference, or adjacent areas (or equivalent
- 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
- Unify inference with RL / post‑training
- 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.
- 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.
Benefits
- 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
Additional details
- 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.
- 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
- 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.
- Have strong expertise in at least one of the following, and are excited to collaborate across (and grow into) the others:
- 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.
- Are comfortable working from algorithms to engines:
- Have a solid research foundation in your area(s) of depth:
- You naturally ask: “Where in the stack is this really bottlenecked?”