Who we are: Glydways is reimagining what public transit can be. We believe that mobility is the gateway to opportunity—connecting people to housing, education, employment, commerce, and care. By making transportation more accessible, affordable, and sustainable, we empower communities to thrive and unlock economic and social prosperity. Our mission is to revolutionize transit with a solution that delivers high capacity, exceptional user experiences, unmatched affordability, and minimal environmental impact. The Glydways system is a groundbreaking network of carbon-neutral, interconnected transit pathways powered by standardized autonomous vehicles on dedicated roadways. Operating 24/7 with on-demand access, it offers personalized and efficient mobility—without the burden of heavy upfront infrastructure costs or ongoing taxpayer subsidies. With Glydways, we’re building more than a transportation system; we’re creating a future where everyone, everywhere, has the freedom to move. Meet the team: The Glydways perception team is responsible for designing and implementing a perception system that includes data fusion from state-of-the-art sensors (e.g., LIDAR, RADAR, High definition cameras, Ultra-wide-band radios, etc.) and robust detection, classification, and tracking of any and all obstacles that could present a hazard to the vehicle (Glydcars) in the system. The perception system for Glydways will reason about information from each Glydcar and from regularly spaced sensor pods monitoring the road network. The perception team plays a vital role within the Autonomy Software engineering team. The team’s deliverables include: - Documenting a detailed design for enacting a safe system that can be certified by public transportation authorities. - Implementing said design on mature prototype vehicles in demonstrations to customers, potential customers, and investors. - Expanding the design to include fail-operational capabilities that extend the overall system to safely give our customers a more comfortable experience. - Managing data collection, autonomy testing, and milestone demonstration events that showcase the system’s maturing capabilities, leading to a production system. Team members may work remotely and must be self-motivated. Roles & Responsibilities: - Implement and own perception algorithms spanning detection, classification, sensor fusion, and multi-object tracking using both ML-based and classical methods - Develop end-to-end perception pipelines from raw sensor input (camera, LiDAR, radar) to structured scene understanding outputs consumed by downstream autonomy modules - Develop and maintain simulation, log-replay, visualization, and offline analysis workflows to maximize test coverage and accelerate validation prior to on-vehicle deployment - Build tooling and infrastructure for dataset management, model training, evaluation, and deployment at scale Knowledge, Skills and Abilities: - Expertise with perception algorithms as shown with academic and/or professional experience - Proficient at translating perception algorithms into production quality C++ (C++14 or newer) code - Strong debugging skills across distributed and asynchronous pipelines, using logs and metrics to drive root-cause analysis. - Excellent verbal and written communication skills Glydways provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are seeking highly motivated MSc or PhD interns to work on video generation and multimodal video foundation models. Interns will focus on one or more components of the foundation model lifecycle and are encouraged to propose creative, research-driven ideas that advance the state of the art. - Contribute to the development and improvement of open-source video foundation models. - Analyze limitations and design scalable solutions. - This is a research-focused internship with opportunities to publish at top-tier computer vision and machine learning conferences. - Work with petabyte-scale video datasets and large distributed GPU clusters with thousands of GPUs. Responsibilities - Research and improve open-source video and multimodal video generation foundation models. - Focus on one or more areas such as: - Pre-training - Supervised fine-tuning - Post-training - Inference - Architecture design - Evaluation - Benchmark models against current state-of-the-art, identify bottlenecks, and propose novel improvements. - Work with large-scale video datasets and distributed training systems. - Collaborate with researchers and engineers on projects with clear research and publication potential. Qualifications - MSc or PhD candidate in Computer Science, Machine Learning, Computer Vision, or a related technical field. - Research topic or experience in image generation, video generation, or multimodal learning. - Awareness of open-source video foundation models and their current limitations. - Proficiency with PyTorch and modern deep learning workflows. - Strong analytical thinking, creativity, and collaboration skills. - Prior first-author related publications in CVPR, ICCV, ECCV, NeurIPS, or ICLR. Preferred Qualifications - Demonstrated related work, such as research codebase or benchmarks released on GitHub or similar platforms. - Experience with large-scale or distributed training. - Hands-on experience with diffusion-based, transformer-based, or hybrid video generation models. Important Information for Candidates - Apply only through our official channels. - We do not use third-party platforms or agencies for recruitment unless clearly stated. - Verify the recruiter’s identity through verified LinkedIn profiles. - Be cautious of unusual communication methods; we do not conduct interviews over WhatsApp, Telegram, or SMS. - Double-check email addresses; all communication will come from emails ending in @tether.to or @tether.io. - We will never request payment or financial details. Team AI Research

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role involves assisting origination staff with condominium document requests and processing. - Communicate clearly and effectively with customers, loan agents, and condo approval team. - Prepare the file by reviewing condominium document requests and contacting HOA/property managers for additional documentation. - Order Questionnaire, HOA insurance, HOA Budget, etc. from property managers/HOA. - Pay for Questionnaire etc. by company credit card or request check from accounting. - Follow up with HOA and package condominium documentation for final review. - Request any additional condominium conditions required by the condo approval team. - Communicate to staff about the status of documents and final review. - Monitor, update, and follow up with pending condo document requests in Project Tracker. Qualifications - Current product knowledge of condominium review process for FHA, VA, Conventional, and other loan programs. - Demonstrate working knowledge of federal and state guidelines, rules, and regulations such as RESPA, TILA, HMDA, etc. - Excellent written and oral communication skills. - Current working knowledge of condominium document review process including recognition of HOA Questionnaires, HOA insurance, HOA Financials, Bylaws, Articles, CC&Rs, etc. - 2+ years' experience in condo document collection, condo project analyst, or equivalent. - Proficient with Encompass or other paperless mortgage loan origination systems. - High School Diploma or equivalent. Requirements - Must have hands-on experience in condominium review. - Must be able to lift up to ten pounds. - Primary functions require sufficient physical ability and mobility to work in an office setting. - Ability to stand or sit for prolonged periods of time. - Occasionally stoop, bend, kneel, crouch, reach, and twist. - Operate office equipment requiring repetitive hand movement and fine coordination including use of a keyboard. - Verbal communication to exchange information. - Vision: See in the normal visual range with or without correction. - Hearing: Hear in the normal audio range with or without correction. Benefits - Pay: $26-$27/hr. DOE - Location: Remote- M-F 8:00 – 5:00 PM Work Authorization - Must be able to verify identity and employment eligibility to work in the U.S.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are looking for a Principal ML Scientist to advance the state of our computer vision systems for warehouse inventory scanning. You will work across the full ML lifecycle — from research and model architecture through training, deployment, and production monitoring — with a focus on delivering measurable improvements to detection, segmentation, and OCR accuracy across our drone and MHE Vision products. - Advance core computer vision model performance (object detection, segmentation, OCR) for warehouse inventory scanning across drone and MHE Vision platforms - Own the full ML lifecycle from research and experiment design through production deployment and monitoring — applying rigorous ablation studies and SOTA methodology - Collaborate with the ML infrastructure team on model optimization and deployment across cloud and edge inference targets (ONNX, TensorRT, quantization) - Work with Operations and Product to understand customer needs and translate them into ML improvements with measurable business impact - Provide technical leadership and mentorship to the ML team, raising standards for experiment design, model evaluation, and production readiness - Explore next-generation perception capabilities, including embedded and on-prem inference optimization for new deployment targets Qualifications - 10+ years of experience in machine learning or computer vision - Deep expertise in CNNs, object detection, image segmentation, and OCR using PyTorch (preferred) or TensorFlow - Strong Python proficiency and software engineering fundamentals; hands-on experience with OpenCV and GPU computing - Track record of delivering production ML systems at scale, including model training, evaluation, and deployment - MS or PhD in Computer Science, Machine Learning, Robotics, or a related field Requirements - Experience with drone, robotics, or autonomous systems perception - Publications in top vision or robotics conferences (CVPR, ICCV, ICRA, NeurIPS, CoRL) - Experience designing and deploying models for real-time inference on constrained compute platforms - Warehouse, logistics, or supply chain domain experience