Job description

About the role 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. As a Research Engineer within Model Shaping, you will develop a platform that enables users to customize open-source models with their own data. 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. 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. 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 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 Add features to inference engines for large-scale post-training experiments, including optimizations for RL workloads 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 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 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 CUDA/Triton/CuTE DSL kernels for inference Developing large-scale and high-load production systems Maintaining or contributing to open-source ML projects Managing machine learning workloads on Kubernetes clusters About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. 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 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. Please see our privacy policy at together.ai

Requirements

Must-haves

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
Add features to inference engines for large-scale post-training experiments, including optimizations for RL workloads
Make sure the service is stable and robust, participating in an on-call rotation and ensuring 24/7 availability of our platform
Have 2+ years of 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
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 CUDA/Triton/CuTE DSL kernels for inference
Developing large-scale and high-load production systems
Maintaining or contributing to open-source ML projects
Managing machine learning workloads on Kubernetes clusters

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlapillmpythongokubernetes

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Health Insurance