Machine Learning Operations Lead

About the Role

Together AI is building the AI Inference & Model Shaping Platform that brings the most advanced generative AI models to the world. Our platform powers multi-tenant serverless workloads and dedicated endpoints, enabling developers, enterprises, and researchers to harness the latest LLMs, multimodal models, image, audio, video, and reasoning models at scale.

We are looking for an exceptional MLOps Engineering Lead to partner closely with our cross-functional engineering, infrastructure, research, and sales teams to ensure excellence of our ML API offerings. Your primary focus will be on delivering world-class inference and fine-tuning in our public APIs and customer deployments by building automation and operations processes.

This role is ideal for a highly motivated and technically adept individual who excels in fast-paced, dynamic environments. You will be in charge of designing and scaling our ML processes & tooling at production scale – optimizing operations to ensure availability and reliability for our services, across differing tenants and user loads, and in a multi-cluster deployment. You will serve as a passionate advocate for internal and external customers, providing feedback to the wider engineering and infrastructure teams to improve our systems and core business metrics. If you thrive in a collaborative, problem-solving environment and are driven to deliver operational excellence, we encourage you to apply for this exciting opportunity.

Responsibilities

• Own availability and performance SLAs for production inference and fine-tuning services across serverless and dedicated deployments
• Own & improve testing, deployment, configuration management, and monitoring practices for multi-cluster ML infrastructure – partnering closely with Infra SREs
• Build self-serve tooling and automation to reduce operational toil and enable internal users (MLOps, customer experience) and self-serve offerings
• Define and enforce configuration best practices for inference engines (vLLM, tvLLM, Pulsar) to prevent runtime issues
• Lead incident response, conduct postmortems, and drive reliability improvements
• Hire, mentor, and grow an MLOps engineering team
• Partner with infrastructure and ML engineering teams to improve system reliability and cost efficiency

Requirements

• 5+ years operating production ML inference or training systems at scale
• 2+ years leading engineering teams, with experience building teams from scratch
• Deep expertise with Kubernetes, multi-cluster orchestration, and ML serving frameworks
• Strong track record owning production SLAs (e.g. availability, TTFT, TPS) 
• Experience with LLM inference serving systems (vLLM, TRT-LLM, or similar)
• Ability to influence cross-functional teams and make deployment/architecture decisions

Nice to Have

• Experience building internal developer platforms or self-serve tooling
• Background in cost optimization for GPU infrastructure
• Contributions to open-source ML infrastructure projects

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $160,000 - $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.

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