Develops and optimizes inference engines for multimodal AI models, integrating new architectures, building scheduling systems, and managing large-scale GPU deployments. Requires strong Python, model serving frameworks like PyTorch/vLLM, and Kubernetes expertise.
188k – 395k/yr
HybridML Engineering
About the role
Role & Responsibilities
Ship new model architectures by integrating them into our inference engine
Collaborate closely across research, engineering and infrastructure to streamline and optimize model efficiency and deployments
Build internal tooling to measure, profile, and track the lifetime of inference jobs and workflows
Automate, test and maintain our inference services to ensure maximum uptime and reliability
Optimize deployment workflows to scale across thousands of machines
Manage and optimize our inference workloads across different clusters & hardware providers
Build sophisticated scheduling systems to optimally leverage our expensive GPU resources while meeting internal SLOs
Build and maintain CI/CD pipelines for processing/optimizing model checkpoints, platform components, and SDKs for internal teams to integrate into our products/internal tooling
Background
Must have:
Strong Python and system architecture skills
Experience with model deployment using PyTorch, Huggingface, vLLM, SGLang, tensorRT-LLM, or similar
Experience with queues, scheduling, traffic-control, fleet management at scale
Experience with Linux, Docker, and Kubernetes
Bonus points:
Experience with modern networking stacks, including RDMA (RoCE, Infiniband, NVLink)
Experience with high performance large scale ML systems (>100 GPUs)
Experience with FFmpeg and multimedia processing
Tech stack
Must have:
Python
Redis
S3-compatible Storage
Model serving (one of: PyTorch, vLLM, SGLang, Huggingface)
Understanding of large-scale orchestration, deployment, scheduling (via Kubernetes or similar)
Nice to have:
CUDA
FFmpeg
Compensation
The base pay range for this role is $187,500 – $395,000 per year.
Builds foundational ML platform infrastructure including model serving pipelines, GPU scheduling systems, and CI/CD for large-scale multimodal AI models. Requires 5+ years in distributed systems with expertise in Python, Kubernetes, and AWS.
188k – 395k/yr
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