Research Engineer, Post-Training Inference
Join Together AI as a Research Engineer to develop a platform for customizing open-source models with user data.
Skills & Technologies
AI in the day-to-day
We build services that enable machine learning developers to choose and improve models using domain-specific data.
Requirements
- Experience
- 2+ years
Benefits
Joblaze summary
In the role of Research Engineer at Together AI, the individual will focus on developing a platform that allows users to customize open-source models with their own data, ensuring a smooth transition from post-training to production. Key skills include experience with modern inference engines and a solid background in Python or Go, alongside familiarity with fine-tuning methods for AI models. This position is ideal for someone with at least two years of experience in deploying machine learning services and a passion for advancing the open-source AI ecosystem.
Joblaze insights
- Listed today on Joblaze — tracked directly from Together AI's career page.
- Together AI has 62 other open roles including Senior Network Engineer, Senior Product Engineer, GTM Engineer.
- 2389 active Python roles on Joblaze right now.
- 1097 active Kubernetes roles on Joblaze right now.
- 815 active Go roles on Joblaze right now.
- Salary band is in line with the typical range for AI/ML roles (median ~$190,850).
Quick facts
- What's the salary range?
- Together AI lists $200,000–$290,000 for this role.
- How much experience is required?
- At least 2 years of relevant experience for this Research Engineer, Post-Training Inference role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: TensorRT-LLM, SGLang, Machine Learning, vLLM, Go, reinforcement learning.
- What seniority level is this role?
- Together AI targets mid-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Research Engineer, Post-Training Inference role at Together AI.
From the original posting
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 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. 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
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 https://www.together.ai/privacy