Platform Engineer, Model Shaping

The Model Shaping team at Together AI works on products and research for tailoring open foundation models to downstream applications. We build services that allow machine learning developers to choose the best models for their tasks and further improve these models using domain-specific data. In addition to that, we develop new methods for more efficient model training and evaluation, drawing inspiration from a broad spectrum of ideas across machine learning, natural language processing, and ML systems.

As a Platform Engineer at Model Shaping, you will work on the foundational layers of Together’s platform for model customization and evaluation. You will design the infrastructure and backend services that will allow us to sustainably and reliably scale the systems powering production workflows launched by our users, as well as internal research experiments.

You will operate in a cross-functional environment, collaborating with other engineers and researchers in the team to improve the infrastructure based on the needs of projects they work on. You will also interact with other engineering teams at Together (such as Commerce, Data Engineering, and Cloud Infrastructure) to integrate the services developed by Model Shaping with systems developed by those teams.

• Design and build Together’s systems and infrastructure for model customization, including user-facing features and internal improvements
• Contribute to reliability improvements for the platform, participating in an on-call rotation and improving processes for incident response
• Create and improve internal tooling for deployment, continuous integration, and observability
• Build a job orchestration platform spanning multiple data centers, supporting a highly heterogeneous hardware landscape
• Partner with teams developing internal services, co-designing these services and incorporating them in systems built by Model Shaping

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 advancements such as FlashAttention, RedPajama, SWARM Parallelism, and SpecExec. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure.

• 3+ years of experience in building infrastructure or backend components of production services
• Comfortable with the fundamentals of Linux environments and modern container/orchestration stacks (e.g., Docker and Kubernetes) 
• Strong software engineering background in Python or Go
• Experienced with infrastructure automation tools (Terraform, Ansible), monitoring/observability stacks (Prometheus, Grafana), and CI/CD pipelines (GitHub Actions, ArgoCD)
• Skilled with analyzing non-trivial issues of complex software systems and documenting your findings
• Have cloud environment (e.g., AWS/GCP/Azure) administration experience, preferably with a hybrid bare-metal/cloud environment
• Strong communication skills, willing to document systems and processes and collaborate with peers of varying technical expertise

• Developing large-scale production systems with high reliability requirements
• Pipeline orchestration frameworks (e.g., Kubeflow, Argo Workflows, Flyte)
• Managing GPU workloads on HPC clusters, ideally with hands-on experience in operating NVIDIA’s networking stack (e.g., NCCL, Mellanox firmware, GPUDirect RDMA)
• Deployment of services for AI training or inference
• Maintaining or contributing to open-source projects

We offer competitive compensation, startup equity, health insurance, and other benefits, as well as flexibility in terms of remote work. 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.