research
Posted 1 weeks agoResearch Engineer, Post-Training Inference
at togetherai
San Francisco, United StatesOn-site
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Responsibilities
- Design and build Together’s systems for customizing open-source models
- Build integrations between the Model Shaping and Inference platforms to ensure a seamless path from post-training to serving production workloads
Requirements
- The Model Shaping team at Together AI works on products and research focused on tailoring open foundation models to downstream applications.
- We build services that enable machine learning developers to choose the best models for their tasks and further improve these models using domain-specific data.
- In addition, we develop new methods for more efficient model training and evaluation, drawing inspiration from a broad range of ideas across machine learning, natural language processing, and ML systems.
- Working across the training and inference stacks, you will build and improve our Fine-Tuning, Reinforcement Learning, and Evaluation services – from ensuring a seamless path from post-training to production serving, to optimizing the inference engine for RL training workloads.
- Above all, you will help build the foundational layer of the open-source AI ecosystem, enabling developers around the world to efficiently create high-quality models tailored to their specific applications. Responsibilities
- experience building and deploying machine learning-based services in a production environment Have hands-on
- experience with modern inference engines, such as SGLang, vLLM, and TensorRT-LLM
- Are familiar with the latest methods for fine-tuning LLMs and other AI models
- Have a strong software engineering background in Python or Go
- Stay up to date with the latest advances and trends in the machine learning community
- Experience in any of the following will make you stand out
- Developing CUDA/Triton/CuTE DSL kernels for inference
- Maintaining or contributing to open-source ML projects
- Managing machine learning workloads on Kubernetes clusters About Together AI
- Together AI is a research-driven artificial intelligence company.
- We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models.
- We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, ATLAS, RedPajama, and Mamba.
- Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.
Experience
- Make sure the service is stable and robust, participating in an on-call rotation and ensuring 24/7 availability of our platform Requirements Have 2+ years of
Benefits
- We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure. Compensation
- We offer competitive compensation, startup equity, health insurance, and other benefits.
- The US base salary range for this full-time position is $200,000 - $290,000.
- Our salary ranges are determined by location, level and role.
- Individual compensation will be determined by experience, skills, and job-related knowledge. Equal Opportunity
Additional details
- As a Research Engineer within Model Shaping, you will develop a platform that enables users to customize open-source models with their own data.
- You will collaborate closely with our product, research, and engineering teams to keep the API reliable, performant, and well integrated into the company's technical infrastructure.
- Add features to inference engines for large-scale post-training experiments, including optimizations for RL workloads
- Serving low-precision (FP4/FP8) models, multiple LoRA adapters within one model instance (Multi-LoRA), or models distributed across several GPU nodes
- Optimizing the performance of RL training workloads
- Developing large-scale and high-load production systems