Build and scale training libraries and infrastructure for autonomous driving models, enabling training on thousands of GPUs and massive datasets.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 224,000 USD – 356,500 USD for Level 5; and 272,000 USD – 431,250 USD for Level 6.
Responsibilities
Crafting, scaling, and hardening deep learning infrastructure libraries and frameworks for training on multi-thousand GPU clusters.
Improving efficiency throughout the training stack: data loaders, distributed training, scheduling, and performance monitoring.
Building robust training pipelines and libraries to handle massive video datasets and enable rapid experimentation.
Collaborating with researchers, model engineers, and internal platform teams to enhance efficiency, minimize stalls, and improve training availability.
Owning core infrastructure components such as orchestration libraries, distributed training frameworks, and fault-resilient training systems.
Develop and support world class Linux RTOS software and real-time tools at both the kernel and application levels for Intel/AMD and ARM64 architectures.
Compensation
The pay range for this position is $85,000 to $100,000 annually with a 10% yearly bonus.
Responsibilities
Develop, enhance, validate, and support Concurrent's RedHawk real-time enhanced Linux kernel and tools
Develop, enhance, validate, and support Concurrent's NightStar real-time tool suite
Build and scale deep learning infrastructure libraries and frameworks for autonomous driving models.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 224,000 USD – 356,500 USD for Level 5; and 272,000 USD – 431,250 USD for Level 6.
Responsibilities
Crafting, scaling, and hardening deep learning infrastructure libraries and frameworks for training on multi-thousand GPU clusters.
Improving efficiency throughout the training stack: data loaders, distributed training, scheduling, and performance monitoring.
Building robust training pipelines and libraries to handle massive video datasets and enable rapid experimentation.
Collaborating with researchers, model engineers, and internal platform teams to enhance efficiency, minimize stalls, and improve training availability.
Owning core infrastructure components such as orchestration libraries, distributed training frameworks, and fault-resilient training systems.
We're looking for a highly capable Backend Engineer to build fast, reliable, production-grade services for a transaction-intensive healthcare and pharmacy-finance platform.
Responsibilities
Performance Engineering: Develop backend services designed for demanding latency and throughput requirements.
Concurrency & Parallelism: Build efficient concurrent systems with careful attention to threading, asynchronous execution, synchronization, and memory utilization.
Profiling & Optimization: Identify and eliminate performance bottlenecks through code profiling, benchmarking, load testing, and runtime analysis.
Cold-Start Performance: Design and optimize initialization, warm-up, and cold-start mechanisms to minimize processing delays.
Decision Systems: Implement reliable APIs, validation workflows, and rules-based processing with predictable and testable behavior.
Requirements
Nice to have: Competitive programming
Nice to have: Payment and transaction-processing systems
Define and drive the AI performance benchmarking and workload characterization strategy across CPU, GPU, APU, NPU, and heterogeneous platforms.
Benchmark AI inference workloads using PyTorch, ONNX Runtime, vLLM, llama.cpp, TensorFlow, and related frameworks and runtimes.
Analyze key performance metrics, including latency, throughput, time to first token, tokens per second, memory utilization, power consumption, and performance per watt.
Profile workloads and identify bottlenecks across AI frameworks, runtimes, libraries, drivers, operating systems, firmware, memory subsystems, and hardware.
Lead complex root-cause analysis and recommend optimizations related to quantization, graph execution, kernels, memory usage, workload scheduling, and heterogeneous compute utilization.
Requirements
Bachelor’s or master’s degree in Computer Engineering, Electrical Engineering, Computer Science, Artificial Intelligence, Machine Learning, or a related technical field
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