Together AI is a research-driven artificial intelligence company. We contribute leading open-source research, models, and datasets to advance the frontier of AI. Our decentralized cloud services…
Together AI is a research-driven artificial intelligence company. We contribute leading open-source research, models, and datasets to advance the frontier of AI. Our decentralized cloud services…
Series B co-led by Prosperity7; reported valuation at $3.3B in coverage
Investor Signal
“Participation from strategic investors including NVIDIA and Salesforce Ventures; Series B led by General Catalyst and co-led by Prosperity7”
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Research Engineer
On-SiteSan Francisco Bay Area, US
On-Site • San Francisco Bay Area, US
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Who you are
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
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
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
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
What the job involves
Benefits
Competitive health insurance plans
Dental and vision insurance
Pre-tax flexible spending accounts
Mental health support and services
Income protection & retirement
401(k) plan
AD&D insurance
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Your current year of study and number of past internship terms will determine your salary band. Actual compensation within each band is based on skills, experience, and academic performance. Exceptional candidates may be considered above the top of their year's range.
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
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
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
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
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
