ML Ops DSX Senior Engineer

Description

The team is hired to work directly with major customers on AI workloads, giving hands-on technical help on advanced deployments and complex distributed systems.

Your goal is to help customers get efficient performance from the company's AI platform across many different setups.

The company works with leading AI firms on their hardest technical problems.

Your work directly affects customer results on AI initiatives. The team is growing quickly.

Responsibilities

• Build and ship custom AI solutions on NCP and Neo Cloud platforms, covering distributed training, inference tuning, and MLOps pipelines based on the company reference designs.
• Serve as the main technical contact for strategic NCPs, provide remote and on-site support, fix complex production issues, and guide partner engineers on platform guidelines.
• Run AI workloads across DGX Cloud, NCP data centers, and major CSP environments using Kubernetes, containers, and GPU scheduling tuned to NCP builds.
• Profile and tune large training and inference jobs on NCP platforms, set up observability and SLO/SLA monitoring, and lead work to cut latency, cost, and operational risk.
• Extend the company reference architectures on partner platforms, build integrations with partner control planes and customer environments, and keep APIs, data pipelines, and enterprise software connected.
• Write implementation guides, runbooks, and post-mortems that set standard methods for running AI workloads at scale on NCP platforms.

Requirements

• Degree (BS, MS, or Ph.D.) in computer science, computer or electrical engineering, or a related technical field, or equivalent experience.
• 8+ years in customer-facing technical roles such as solutions engineering, DevOps, site reliability, or ML infrastructure engineering, ideally with large-scale cloud or service provider environments.
• Strong skills in Linux, distributed computing, Kubernetes, containers, and GPU scheduling on multi-tenant or service-provider platforms.
• Proven AI/ML experience running large training and inference workloads in production or critical settings, such as LLMs, generative models, or recommendation systems.
• Solid Python or Go programming, with hands-on use of PyTorch or TensorFlow for training and serving.
• Ability to work with customer and partner engineers in fast-paced settings, lead complex investigations, and take issues through to root cause and fix.
• Clear communication and presentation skills, able to explain architectures, trade-offs, and recommendations to engineers and leaders.
• Ways to stand out: experience with the company ecosystem, including DGX systems, CUDA, NeMo, Triton, NIM, and networking such as InfiniBand and RoCE.
• Ways to stand out: direct work with the company Cloud Partners, hyperscale CSPs, or managed AI cloud platforms, including implementing the company reference architectures for AI infrastructure.
• Ways to stand out: familiarity with MLOps and cloud-native practice, such as containers, CI/CD, observability stacks (Prometheus, Grafana, OpenTelemetry), and GitOps.
• Ways to stand out: infrastructure as code (Terraform, Ansible, or similar) to deploy and configure GPU-accelerated clusters and NCP building blocks repeatably.

Conditions

Base salary depends on location, experience, and pay for similar roles: $184,000-$287,500 USD for Level 4, or $224,000-$356,500 USD for Level 5.

This is an existing vacancy, and applications are accepted at least until November 1, 2026.

The company uses AI tools in its recruiting process.

The company is an equal opportunity employer and does not discriminate on any legally protected characteristic.

Benefits

• Competitive salary and a generous benefits package.