AI Infrastructure Engineer
Country
United States
Location type
Remote
Salary
$170,000 – $210,000 base · Annual
About the Company
Fast-growing AI company supported by NVIDIA, enabling data centers to dynamically manage power and maximize compute capacity from existing energy infrastructure.
About the Role
The AI Infrastructure Engineer is responsible for designing, building, and owning the end-to-end infrastructure that serves the company's AI and ML models across edge deployments, cloud environments, and data center integrations. They are also responsible for designing, building, and owning the integration of power data with AI inference software. This is the first dedicated role of this kind, and will serve as the foundational function for how the company deploys and operates AI capabilities in production. The role requires deep technical expertise in ML model serving, distributed systems, and GPU infrastructure, with a strong emphasis on reliability, performance, and scalability. This position works cross-functionally with product, engineering, and data science teams and is open to fully remote candidates, with periodic travel expected for company retreats and key on-site engagements.
Responsibilities
Lead the design and build of the company's AI inference platform — establishing architecture patterns, deployment standards, and operational practices that will scale with the company
Own end-to-end model serving infrastructure for the company's AI infrastructure (on-prem and datacenter)
Build and maintain fault-tolerant, high-performance systems for serving AI models at scale, with a focus on low latency, reliability, and cost efficiency
Collaborate closely with algorithms engineers to integrate AI inference data and configuration with power optimization algorithms
Optimize GPU utilization and inference performance across our hardware fleet, including NVIDIA accelerators central to the company's edge AI platform
Establish MLOps best practices including CI/CD pipelines for model deployment, monitoring, and rollback across environments
Contribute to infrastructure roadmap decisions, including build vs. buy tradeoffs, tooling selection, and platform evolution as the team grows
Requirements
5+ years of software engineering experience with a strong focus on AI infrastructure, backend systems, or distributed systems
Hands-on experience with AI model serving frameworks (e.g., vLLM, SGLang, Triton, TensorRT, TorchServe, or similar)
Understanding of container orchestration and cluster management (Kubernetes, Docker)
Experience deploying and operating infrastructure across both datacenter and on-prem environments
Strong knowledge of GPU workloads and the tradeoffs that come with them — you understand how inference differs from training, and why it matters
Proficiency in Python; C++, CUDA, Go, Rust a plus
Excellent communication skills and comfort working cross-functionally in a lean, fast-moving environment
Willingness to travel up to 10% of time
Nice to Have
Dynamo experience a plus
Experience with edge AI deployments or constrained compute environments
Familiarity with infrastructure as code (Terraform, Helm)
Experience with observability platforms (Datadog, Prometheus, Grafana)
Background in energy, utilities, or industrial IoT
Contributions to open-source ML infrastructure projects
What the Company Offers
Diverse and inclusive workplace that is welcoming, supportive, affirming and respectful
Empowering employees to solve problems and work together to make a difference
Providing mentorship and growth opportunities as part of a collaborative team
A flexible work environment with flexible paid time off
Competitive compensation and benefits, including health, dental, vision, and employer-match 401k