Senior Software Engineer, Observability at Together AI

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

06 Oct, 26

Salary

280000.0

Posted On

08 Jul, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Prometheus, Grafana, ClickHouse, OpenTelemetry, Go, Python, Terraform, Ansible, Helm, Kubernetes, Docker, Distributed Systems, PostgreSQL, MongoDB, Redis, CI/CD

Industry

Software Development

Description

About the Role Together AI is building the AI Acceleration Cloud, an end-to-end platform for the full generative AI lifecycle, combining the fastest LLM inference engine with state-of-the-art AI cloud infrastructure. The AI Infrastructure team at Together AI is at the forefront of building and scaling the foundational systems that power our generative AI platform. The storage and observability team is crucial for designing, implementing, and maintaining robust distributed storage solutions, ensuring seamless data access and management. They are also responsible for developing comprehensive observability platforms, providing critical insights into system performance and GPU utilization, and proactively identifying and resolving issues. Responsibilities Design and implement a scalable observability platform (metrics, logs, traces) using tools like Prometheus, Grafana, ClickHouse, ClickStack, and OpenTelemetry, including telemetry data pipelines and log aggregation workflows. Develop automated monitoring, alerting, and anomaly detection systems, including SLIs/SLOs, runbooks, and predictive analytics for critical services. Build and deploy custom observability tools and infrastructure-as-code using Go, Python, Terraform, Ansible, and Helm. Collaborate with engineering teams to enhance distributed tracing and application monitoring, and lead incident response with post-mortem analysis. Define observability best practices. Requirements Expertise in observability platforms (Prometheus, Grafana, ClickStack, OpenTelemetry) and cloud-native monitoring services (AWS, GCP, Azure). Strong programming skills in Go, Python, or similar languages, with proficiency in infrastructure-as-code tools (Terraform, Ansible, Helm). Experience designing, operating, and scaling large-scale distributed systems and pipelines for high-volume data ingestion and real-time querying. Deep understanding of containerization (Docker) and orchestration (Kubernetes). Knowledge of microservices architecture, service mesh technologies, CI/CD pipelines, and GitOps workflows. Expertise in managing databases (PostgreSQL, MongoDB, Redis) and time-series databases with high-cardinality data. Preferred Experience monitoring AI/ML infrastructure, GPU clusters, and custom metrics for model performance and training pipelines. Background in high-frequency, low-latency systems monitoring, chaos engineering, and reliability testing. Contributions to open-source observability projects. Familiarity with security monitoring and compliance frameworks. Compensation 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 - $280,000 + equity + benefits. 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

Responsibilities

Design and implement a scalable observability platform for metrics, logs, and traces to monitor system performance and GPU utilization. Develop automated monitoring, alerting systems, and custom infrastructure-as-code tools to enhance distributed tracing and incident response.