AI Researcher, Core ML (Turbo)

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.

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.