Veear
Designing, implementing, and maintaining distributed systems to build world-class ML platforms/products at scale
Experiment with, deploy, and manage LLMs in a production context
Benchmark and optimize inference deployments for different workloads, e.g. online vs. batch vs. streaming workloads
Diagnose, fix, improve, and automate complex issues across the entire stack to ensure maximum uptime and performance
Design and extend services to improve functionality and reliability of the platform
Monitor system performance, optimize for cost and efficiency, and resolve any issues that arise
Build relationships with stakeholders across the organization to better understand internal customer needs and enhance our product better for end users
Minimum Qualifications:
5+ years of experience in distributed systems with deep knowledge in computer science fundamentals Experience managing deployments of LLMs at scale Experience with inference runtimes/engines, e.g. ONNXRT, TensorRT, vLLM, sglang Experience with ML Training/Inference profiling and optimization for different workloads and tasks, e.g. online inference, batch inference, streaming inference Experience with profiling ML models for different end use cases, e.g. RAG vs. code completion, etc. Experience with containerization and orchestration technologies, such as Docker and Kubernetes. Experience in delivering data and machine learning infrastructure in production environments Experience configuring, deploying, and troubleshooting large scale production environments Experience in designing, building, and maintaining scalable, highly available systems that prioritize ease of use Experience with alerting, monitoring and remediation automation in a large-scale distributed environment Extensive programming experience in Java, Python or Go Strong collaboration and communication (verbal and written) skills B.S., M.S., or Ph.D. in Computer Science, Computer Engineering, or equivalent practical experience Preferred Qualifications:
Understanding of the ML lifecycle and state of the art ML Infrastructure technologies Familiarity with CUDA + kernel implementation Experience with inference optimization and fine-tuning techniques (e.g. pruning, distilling, quantization) Experience with deploying + optimizing ML models on heterogenous hardware, e.g. GPUs, TPUs, Inferentia, etc. Experience with GPU and other type of HPC infrastructure Experience with training framework like PyTorch, Tensorflow, JAX Deep understanding of Ray and KubeRay
5+ years of experience in distributed systems with deep knowledge in computer science fundamentals Experience managing deployments of LLMs at scale Experience with inference runtimes/engines, e.g. ONNXRT, TensorRT, vLLM, sglang Experience with ML Training/Inference profiling and optimization for different workloads and tasks, e.g. online inference, batch inference, streaming inference Experience with profiling ML models for different end use cases, e.g. RAG vs. code completion, etc. Experience with containerization and orchestration technologies, such as Docker and Kubernetes. Experience in delivering data and machine learning infrastructure in production environments Experience configuring, deploying, and troubleshooting large scale production environments Experience in designing, building, and maintaining scalable, highly available systems that prioritize ease of use Experience with alerting, monitoring and remediation automation in a large-scale distributed environment Extensive programming experience in Java, Python or Go Strong collaboration and communication (verbal and written) skills B.S., M.S., or Ph.D. in Computer Science, Computer Engineering, or equivalent practical experience Preferred Qualifications:
Understanding of the ML lifecycle and state of the art ML Infrastructure technologies Familiarity with CUDA + kernel implementation Experience with inference optimization and fine-tuning techniques (e.g. pruning, distilling, quantization) Experience with deploying + optimizing ML models on heterogenous hardware, e.g. GPUs, TPUs, Inferentia, etc. Experience with GPU and other type of HPC infrastructure Experience with training framework like PyTorch, Tensorflow, JAX Deep understanding of Ray and KubeRay