ML Infrastructure Engineer
ML Infrastructure Engineer
Menlo Park, CA | On-Site | Full-Time/Direct Hire
Client Opportunity | Through Phizenix
Phizenix, a certified minority and women-led recruiting firm, is hiring on behalf of an AI startup pioneering diffusion-based large language modelsbuilt for faster generation, multimodal integration, and scalable enterprise deployment.
We're looking for a
ML Infrastructure Engineer
to help build the infrastructure that powers large-scale model training and real-time inference. You'll collaborate with world-class researchers and engineers to design high-performance, distributed systems that bring advanced LLMs into production.
Responsibilities
Design and manage distributed infrastructure for ML training at scale
Optimize model serving systems for low-latency inference
Build automated pipelines for data processing, model training, and deployment
Implement observability tools to monitor performance in production
Maximize resource utilization across GPU clusters and cloud environments
Translate research requirements into robust, scalable system designs
Must-Haves
MS or PhD
in Computer Science, Engineering, or a related field (or equivalent experience)
Strong foundation in software engineering, systems design, and distributed systems
Experience with cloud platforms (AWS, GCP, or Azure)
Proficient in Python and at least one systems-level language (C++/Rust/Go)
Hands-on experience with Docker, Kubernetes, and CI/CD workflows
Familiarity with ML frameworks like PyTorch or TensorFlow from a systems perspective
Understanding of GPU programming and high-performance infrastructure
Nice-to-Haves
Experience with large-scale ML training clusters and GPU orchestration
Knowledge of LLM-serving tools (vLLM, TensorRT, ONNX Runtime)
Experience with distributed training strategies (e.g., data/model/pipeline parallelism)
Familiarity with orchestration tools like Kubeflow or Airflow
Background in performance tuning, system profiling, and MLOps best practices
At
Phizenix
, we're committed to supporting diverse and inclusive teams. This is your chance to shape the systems that power the next generation of AI innovation. Let's build the futuretogether.
$180,000
$200,000 USD
Employers have access to artificial intelligence language tools (AI) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
Report this job
Dice Id:
91165417
Position Id:
...
ML Infrastructure Engineer
Menlo Park, CA | On-Site | Full-Time/Direct Hire
Client Opportunity | Through Phizenix
Phizenix, a certified minority and women-led recruiting firm, is hiring on behalf of an AI startup pioneering diffusion-based large language modelsbuilt for faster generation, multimodal integration, and scalable enterprise deployment.
We're looking for a
ML Infrastructure Engineer
to help build the infrastructure that powers large-scale model training and real-time inference. You'll collaborate with world-class researchers and engineers to design high-performance, distributed systems that bring advanced LLMs into production.
Responsibilities
Design and manage distributed infrastructure for ML training at scale
Optimize model serving systems for low-latency inference
Build automated pipelines for data processing, model training, and deployment
Implement observability tools to monitor performance in production
Maximize resource utilization across GPU clusters and cloud environments
Translate research requirements into robust, scalable system designs
Must-Haves
MS or PhD
in Computer Science, Engineering, or a related field (or equivalent experience)
Strong foundation in software engineering, systems design, and distributed systems
Experience with cloud platforms (AWS, GCP, or Azure)
Proficient in Python and at least one systems-level language (C++/Rust/Go)
Hands-on experience with Docker, Kubernetes, and CI/CD workflows
Familiarity with ML frameworks like PyTorch or TensorFlow from a systems perspective
Understanding of GPU programming and high-performance infrastructure
Nice-to-Haves
Experience with large-scale ML training clusters and GPU orchestration
Knowledge of LLM-serving tools (vLLM, TensorRT, ONNX Runtime)
Experience with distributed training strategies (e.g., data/model/pipeline parallelism)
Familiarity with orchestration tools like Kubeflow or Airflow
Background in performance tuning, system profiling, and MLOps best practices
At
Phizenix
, we're committed to supporting diverse and inclusive teams. This is your chance to shape the systems that power the next generation of AI innovation. Let's build the futuretogether.
$180,000
$200,000 USD
Employers have access to artificial intelligence language tools (AI) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
Report this job
Dice Id:
91165417
Position Id:
...
Job ID: 480288474
Originally Posted on: 6/7/2025
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