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Together AI

Research Engineer, Core ML

Research Engineer building production ML systems at the intersection of efficient inference, RL/post-training, and serving engines. Translates algorithms into scalable infrastructure improving latency, throughput, and model quality. Requires 3+ years ML systems experience and advanced degree.

200k – 280kSan Francisco, CAML EngineeringOnsite3+ YOE
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About the role

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, speculative decoding like ATLAS, quantization).
    • Profile and optimize performance across GPU, networking, and memory layers.

  • Unify inference with RL / post-training

    • Design and operate RL and post-training pipelines (e.g., RLHF, RLAIF, GRPO, DPO-style methods, reward modeling).
    • Optimize RL workloads with inference-aware techniques like async rollouts and speculative decoding.
    • Train, evaluate, and iterate on frontier models.
    • Co-design algorithms and infrastructure to identify bottlenecks.
    • Run ablations and scale-up experiments.

  • Own critical systems at production scale

    • Profile, debug, and optimize under real workloads.
    • Drive roadmap items requiring engine modifications.
    • Establish metrics, benchmarks, and experimentation frameworks.

  • Provide technical leadership (Staff level)

    • Set technical direction for cross-team efforts.
    • Mentor engineers and researchers.

Requirements

Deep expertise in one or more areas with breadth to work across the stack:

  • Bias toward implementation and shipping.
  • Expertise in: large-scale inference systems (SGLang, vLLM), RL/post-training for LLMs (GRPO, RLHF), model architecture, distributed systems/HPC for ML.
  • Strong Python coding, performance profiling/optimization.
  • Research foundation with track record (papers, open-source, production).

Minimum qualifications

  • 3+ years experience in ML systems, model training/inference, or equivalent.
  • Advanced degree in Computer Science, EE, or related field, or equivalent.
  • Experience owning complex technical projects end-to-end.

Compensation

US base salary range: $200,000 - $280,000 + equity + benefits.

Skills

PythonSglangvLLMRLHFGrpoDpoSpeculative DecodingAtlasGpu OptimizationDistributed Systems

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