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Research Engineer, Post-Training Inference
Summary by AIEngineer.careers
Research Engineer, Post-Training Inference is a MLOps Engineer role (full-time). with Together AI. in SAN FRANCISCO, US. Compensation shown: $200K–$290K. Imported listing (source: aihiringboard.com). Apply on the employer's site (aihiringboard.com).
Imported listing (source: aihiringboard.com) · Apply on aihiringboard.com
SAN FRANCISCO · US
$200K–$290K
Full-time
MID
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from aihiringboard.com
Together AI is seeking a Research Engineer to join the Model Shaping team, focusing on optimizing post-training inference and fine-tuning services for open-source foundation models. You will build platforms that enable developers to customize models using domain-specific data, bridging the gap between training and production serving. The role involves deep work with modern inference engines and reinforcement learning workloads.
Responsibilities
- Design and build systems for customizing open-source models
- Build integrations between Model Shaping and Inference platforms for seamless production serving
- Add features to inference engines for large-scale post-training experiments and RL optimizations
- Ensure platform stability, robustness, and 24/7 availability through on-call rotation
Requirements
- 2+ years of experience building and deploying ML-based services in production
- Hands-on experience with modern inference engines (SGLang, vLLM, TensorRT-LLM)
- Familiarity with latest LLM fine-tuning methods
- Strong software engineering background in Python or Go
Nice to Have
- Experience serving low-precision models (FP4/FP8) or Multi-LoRA setups
Together AI
San Francisco · US · 434+ employees
Together AI is an AI-native cloud platform that provides tools for generative AI development, including inference acceleration, model shaping, and pre-training. It offers GPU clusters and an inference engine to help developers scale AI applications.
