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Research Engineer, Core ML

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Together AI

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Location:
United States , San Francisco

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Category:
IT - Software Development

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Contract Type:
Not provided

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Salary:

200000.00 - 280000.00 USD / Year
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Job Description:

This is a research engineering role with direct production impact. You will translate new RL algorithms, scheduling methods, and inference optimizations into production-grade systems that power Together’s API. Success means shipping measurable improvements in latency, throughput, cost, and model quality at scale. The Core ML (Turbo) team sits at the intersection of efficient inference and post‑training / RL systems, building and operating the systems behind Together’s API.

Job Responsibility:

  • 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
  • Profile and optimize performance across GPU, networking, and memory layers
  • Unify inference with RL / post‑training
  • Design and operate RL and post‑training pipelines
  • Make RL and post‑training workloads more efficient with inference‑aware training loops
  • Co‑design algorithms and infrastructure
  • Run ablations and scale‑up experiments to understand trade‑offs
  • 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
  • Establish metrics, benchmarks, and experimentation frameworks
  • Provide technical leadership (Staff level)
  • Set technical direction for cross‑team efforts
  • Mentor other engineers and researchers

Requirements:

  • 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
  • Strong expertise in at least one of the following: Large‑scale inference systems (e.g., SGLang, vLLM, FasterTransformer, TensorRT, custom engines, or similar), GPU performance, distributed serving
  • RL / post‑training for LLMs or large models (e.g., GRPO, RLHF/RLAIF, DPO‑like methods, reward modeling)
  • Model architecture design for Transformers or other large neural nets
  • Distributed systems / high‑performance computing for ML
  • Strong coding ability in Python
  • Experience profiling and optimizing performance across GPU, networking, and memory layers
  • Track record of impactful work in ML systems, RL, or large‑scale model training (papers, open‑source projects, or production systems)

Nice to have:

  • Bias toward implementation and shipping
  • Comfortable working from algorithms to engines
  • Able to take a new sampling method, scheduler, or RL update and turn it into a production‑grade implementation
  • Solid research foundation in your area(s) of depth
  • Can read new RL / post‑training papers, understand their implications on the stack, and design minimal, correct changes
  • Operate well as a full‑stack problem solver
  • Enjoy collaborating with infra, research, and product teams
What we offer:
  • Startup equity
  • Health insurance
  • Competitive benefits

Additional Information:

Job Posted:
March 10, 2026

Employment Type:
Fulltime
Work Type:
Hybrid work
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