B
Blue River Technology
Senior Machine Learning Engineer
Skills
Deep LearningPyTorchTensorFlowMachine Learning
2 months ago
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Important information
Contract type:
Permanent
Salary:
$149,365 – $275,000 / year
Location:
Santa Clara, CA, USA
Work mode:
Onsite
Published on:
2 September 2026
What they need
- Implement deep-learning models and frameworks for scene segmentation, depth estimation, active learning, and the development of situational awareness for autonomous vehicles operating in construction and agriculture environments
- Build and evaluate machine-learning models using data from multiple sensor modalities, including vision, radar, and thermal cameras, and assess trade-offs in accuracy, robustness, and latency
- Research and develop new methods to improve detection performance and increase processing speed
- Collaborate with robotics engineers to transition algorithms from desktop and server-class systems to real-time field-deployed robotic platforms
- Work with systems and software engineers to design and maintain data-processing and annotation pipelines that support continuous system monitoring, evaluation, and improvement- Integrate and validate synthetic image datasets for computer vision model training, including domain alignment, data normalization, camera parameter adjustment, and evaluation of generalization performance against real-world datasets (1 yr, 6 mos)
- Research and implement emerging computer vision architectures and training strategies, including transformer-based backbones, advanced loss functions, and optimization techniques, to improve model accuracy and inference efficiency (1 yr, 6 mos)
- Train and optimize computer vision models for object detection, semantic segmentation, and monocular/stereo depth estimation using supervised and self-supervised learning, including loss function customization and multi-scale model training (1 yr, 6 mos)
- Perform dataset curation, large-scale image preprocessing, exploratory error analysis, and implement active learning strategies based on model uncertainty and diversity sampling to improve training data quality and reduce false positives (1 yr, 6 mos)
- Evaluate model inference performance and runtime behavior across heterogeneous hardware platforms, including high-performance computing (HPC) GPU environments, local NVIDIA GPU development systems, and VPU-based inference on production deployment machines, to ensure real-time execution requirements are met (1 yr, 6 mos)
- Required skills:
- Design and implement end-to-end ML pipelines for data ingestion, preprocessing, model training, validation, experiment tracking, and deployment using reproducible workflows and version-controlled environments (1 yr, 6 mos)
- Build and evaluate deep learning models using PyTorch and TensorFlow, implementing transfer learning, custom training loops, distributed training, gradient-based optimization, and hyperparameter tuning for large-scale image datasets (1 yr, 6 mos)
- Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, or related field plus 1 year and 6 months of related experience
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