Jobs at Together AI

Design and implement Elixir APIs and services, maintain BEAM production systems, build service SDKs, improve integrations, ensure high code quality, and work with PostgreSQL and MongoDB while contributing to authentication/authorization features.

Operate and scale Together AI's production infrastructure: run GPU-enabled Kubernetes clusters, automate with Ansible and Terraform, build monitoring/observability, respond to incidents (on-call), debug production issues, design deployment/upgrade processes, and plan infrastructure growth for high availability and scalability.

As a Senior Developer Productivity Engineer, you will enhance CI/CD processes, optimize workflows, and build tools for efficient software delivery.

The Site Reliability Engineer ensures user-facing services run smoothly, focuses on systems reliability and scalability, and enhances operational practices through automation and engineering principles.

Provide customer-facing SRE/technical support for Kubernetes GPU clusters and HPC environments. Troubleshoot GPU hardware, networking (InfiniBand/RDMA/NVLink), distributed storage (Weka/NFS), Slurm workflows, and container workloads. Maintain cluster health, perform node maintenance and migrations, document procedures, and collaborate with Engineering, Product, and Sales to drive customer success and product improvements. Provide weekend and on-call coverage.

Customer-facing technical role providing SRE-style support for GPU clusters, LLM inference, and fine-tuning. Troubleshoot production incidents, run infra-as-code changes, validate migrations, monitor observability dashboards, and translate technical findings to customers and engineering. Drive improvements by surfacing patterns and maintaining documentation.

Partner with strategic customers to optimize LLM inference and post-training pipelines, tune inference engines for latency/throughput, run and productionize fine-tuning (LoRA, SFT, DPO, RLHF), and feed field insights into product and model roadmaps to ensure successful POCs and deployments.

Provide legal support for infrastructure procurement and enterprise commercial deals. Lead negotiations for GPU/cloud capacity, data center, networking, and vendor agreements; align vendor terms with customer contracts; manage export controls and data/privacy compliance; build templates, playbooks, and scalable procurement and sales workflows; support partnerships, reseller and go-to-market deals; and run procurement processes and vendor template libraries.

This role involves optimizing model serving layers for voice AI applications, working with inference engines, and improving performance for STT and TTS systems.

The Staff Machine Learning Engineer will optimize the inference layer for voice applications, focusing on model serving and real-time audio processing. Responsibilities include driving performance architecture, collaborating with model partners, and developing evaluation frameworks for STT and TTS models.

Design, deploy, and maintain large-scale hybrid HPC/data-center networks. Troubleshoot and analyze network issues, recommend technologies, develop automation, ensure reliability, manage vendor relationships, and lead complex network projects.

Provide hands-on IT support for in-office and remote employees across identity, devices, SaaS, and procurement. Administer Okta and MDM (macOS/Linux), manage asset lifecycle, create documentation and automations, support onboarding/offboarding, basic network/A/V troubleshooting, and contribute to security hygiene and IT projects.

Design and implement a scalable observability platform (metrics, logs, traces) using Prometheus, Grafana, ClickHouse/ClickStack, and OpenTelemetry. Build telemetry pipelines, automated monitoring, SLIs/SLOs, alerting, anomaly detection, and runbooks. Develop IaC and custom tools with Go, Python, Terraform, Ansible, and Helm. Collaborate on distributed tracing, lead incident response, and define observability best practices.

Design, deploy, and maintain large-scale hybrid data center networks; troubleshoot and analyze network issues; develop automation and tooling; evaluate and recommend hardware/software; manage vendor relationships; ensure reliability, scalability, and compliance; lead complex network projects and contribute to architecture and roadmaps.

Research intern contributing to agentic AI: develop and implement novel training methods, design experiments and datasets, evaluate agent behavior and safety, document findings, and communicate results to the team.

The Manager will analyze and benchmark compute infrastructure sourcing and operations, driving strategic decisions and optimizing resource allocation across sites and vendors.

Work on developing and optimizing GPU-accelerated kernels and algorithms for ML/AI. Collaborate with modeling, hardware, and software teams to co-design GPU kernels and architectures, integrate GPU solutions, and profile and optimize performance using GPU profiling tools.

Develop and maintain a platform for customizing open-source models, integrating Model Shaping with Inference, adding inference engine features and RL optimizations, ensuring production stability and 24/7 availability, and collaborating with product, research, and engineering teams to support fine-tuning, RL, and evaluation workflows.

As an AI Infrastructure Engineer, you will manage user-facing services, ensure system reliability, and improve infrastructure using automation tools such as Ansible and Terraform while collaborating with various stakeholders.

Build and ship customer-facing visibility features (events, audit logs, timelines, notifications, analytics) for Together's Cloud. Partner with Data Platform and Observability teams to implement data collection and processing, own scoped backend work end-to-end, write well-tested backend and product code, and maintain data freshness, completeness, and correctness for trustworthy customer insights.