Research Engineer, Post-Training Inference

Together AI
San Francisco, CA

Who this role is best for

Best suited to mid-level engineers with production ML experience who thrive in collaborative research environments and have expertise in modern inference engines.

Best fit for

  • Engineers who bridge model training and inference stacks in production settings.
    — “Working across the training and inference stacks
  • Candidates comfortable with on-call rotations for critical AI infrastructure.
    — “participating in an on-call rotation and ensuring 24/7 availability
  • Developers who actively track emerging ML optimization techniques.
    — “Stay up to date with the latest advances and trends

Things to consider

  • Requires hands-on experience with specific inference engines like vLLM.
    — “hands-on experience with modern inference engines, such as SGLang, vLLM
  • Involves optimizing performance for specialized RL training workloads.
    — “optimizing the inference engine for RL training workloads

How to stand out

  • Highlight concrete optimizations you've implemented for low-precision models.
    — “Serving low-precision (FP4/FP8) models
  • Demonstrate contributions to open-source ML projects in your portfolio.
    — “Maintaining or contributing to open-source ML projects
  • Showcase Kubernetes experience for ML workload management.
    — “Managing machine learning workloads on Kubernetes clusters
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · TeamLevel · Mid Level

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • Developing efficient inference engines
  • Optimizing model training and evaluation
Typical background
Experience in machine learning engineeringBackground in software development

Skills & requirements

Required

Machine LearningInference EnginesFine-tuning ModelsReinforcement Learning

Preferred

CUDA DevelopmentKubernetes Clusters

Stack & domain

Machine LearningFine-tuning LlmsInference EnginesSglangVllmTensorrt-llmCudaTritonCute Dsl KernelsKubernetes ClustersCollaborationCommunicationTeamworkNatural Language ProcessingMl SystemsAi Infrastructure

About the role

Original posting from Together AI via Greenhouse

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

Source: Together AI careers (Greenhouse)

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