Jobs at Together AI

Design and implement manifest-driven provisioning and reconciliation systems for GPU hosts and clusters. Build durable workflow engines, declarative self-service APIs, event-driven automation, self-healing pipelines, and production-grade control planes. Own coding, testing, CI/CD, and operate the platform in production to eliminate manual provisioning.

Provide technical support to customers on AI solutions, resolve complex issues, collaborate with teams, and improve product offerings.

Own full sales cycle for strategic AI-native and enterprise accounts: generate pipeline, close new business, expand customers, and partner with Solutions, Engineering, and Research to run POCs and scale deployments. Provide technical and business insights to inform product and sales strategy.

Manage day-to-day San Francisco office operations, support employees, coordinate facilities maintenance, manage access and mail, run food/beverage and swag programs, and assist planning internal events.

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

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.

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.

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 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.

As a Senior Platform Engineer at Together AI, you will build and manage the API and infrastructure layer for a Voice AI platform, focusing on real-time streaming capabilities and developer experience, ensuring reliability for production voice applications.

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.

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.

The Staff Platform Engineer will architect the Voice AI platform, focusing on real-time API reliability, autoscaling, and enhanced developer experience while collaborating on voice infrastructure and ML serving.

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.

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.

Work on distributed inference and compiler-aware optimizations for foundation models. Design and run experiments, implement cross-layer model-system-hardware optimizations (e.g., KV cache, speculative decoding), communicate results to the team, and document findings in publications and blog posts.

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.

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

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.