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Top Deep Learning Jobs in Los Angeles, CA
Aerospace • Artificial Intelligence • Machine Learning • Robotics • Software • Defense Technology
Develop deep learning and 3D computer vision models for state estimation, visual navigation, localization, depth, correspondence, and pose estimation. Own data preparation, training, evaluation, benchmarking, and reproducible workflows. Integrate learned measurements into VIO and terrain-relative navigation systems, optimize models for onboard compute, and collaborate with deployment, software, systems, and flight-test teams to deliver production-ready Hivemind SDK components.
Top Skills:
3D Computer VisionDeep LearningDiffusion ModelsEmbedded Compute PlatformsFlow MatchingGpu ComputingModel QuantizationMulti-View GeometryOnnxPythonPyTorchSensor Data PipelinesSlamStructure From Motion (Sfm)TensorrtVisual-Inertial Odometry (Vio)
Artificial Intelligence • Cloud • Healthtech • Machine Learning
Develop and productionize deep learning models for computer vision and NLP on medical imaging products. Design and run experiments, document results, collaborate on data labeling, write deployment/docker code, optimize inference speed, and conduct DL methodology research.
Top Skills:
BertConfluenceDockerHalf-Precision TrainingNetwork QuantizationPythonPyTorchTensorFlowTransformers
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
Build, scale, and harden deep learning training infrastructure for multi-thousand GPU clusters. Improve data loaders, distributed training, scheduling, and performance monitoring. Develop fault-resilient orchestration, training pipelines for massive video datasets, and collaborate with researchers and platform teams to maximize training efficiency, availability, and scalability.
Top Skills:
DdpFsdpGpuInfinibandKubernetesLustreNcclPipeline ParallelismPythonPyTorchRoceSlurmTensor Parallelism
Automotive • Other
Lead design and training of large-scale transformer and hybrid foundation models across text and multimodal domains. Own architecture choices, diagnose large-scale training instabilities, optimize scaling tradeoffs (data, compute, model), design loss and alignment strategies, and execute distributed training (FSDP/ZeRO, tensor/pipeline parallelism) with mixed precision to build in-house foundation model expertise.
Top Skills:
AdafactorAdamwAlibiBf16Chinchilla ScalingContrastive ObjectivesDpoFp8FsdpGradient CheckpointingGrouped Query Attention (Gqa)GrpoKv CacheLionMixed PrecisionMixture-Of-Experts (Moe)Next-Token PredictionPipeline ParallelismRlhfRopeSsmTensor ParallelismTransformersZero-3
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