Sr Machine Learning Engineer

Role Summary

Title and Location: Sr Machine Learning Engineer in Santa Clara, CA.

Responsibilities

• 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.

Requirements

• Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, or related field plus 1 year and 6 months of related experience.
• Required skills:
• 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).
• 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).
• 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).
• 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).
• 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).
• 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).
• 5% domestic travel required to visit testing facilities and customer sites. May work remotely; periodic time in office required; must live within commuting distance of office.