Kablio AI
AI Turbo Recruiter
Want to hear how I work? Hit play.Kablio AI applies for you. You just show up to the interviewKablio AI helps you secure roles in construction, clean energy, facilities management, engineering, architecture, sustainability, environment and other physical world sectors.
Get hired, get rewarded!
Land a job through Kablio and earn a 5% salary bonus.
Exclusive benefits
5%Bonus
Senior Machine Learning/Computer Vision Engineer
Parallel Systems
Develop and deploy advanced perception ML models for autonomous rail vehicles.
7d ago
$150,000 - $240,000
Experienced (8-12 years), Expert & Leadership (13+ years)
Full Time
Los Angeles, CA
Hybrid
Company Size
45 Employees
Service Specialisms
No specialisms available
Sector Specialisms
Railroads
Freight
Autonomous Rail
Battery-Electric Rail Vehicles
Logistics
Supply Chain
Ports
Transportation Infrastructure
Role
What you would be doing
ml lifecycle
training pipelines
model development
research evaluation
architecture design
data infrastructure
- Collaborate cross-functionally with other engineering, research, and product teams to ensure seamless integration of ML systems into real-world applications.
- Own the full ML lifecycle—from data mining and annotation to training, evaluation, and deployment of production-grade models.
- Develop scalable and efficient training pipelines that ensure robust, real-time inference performance.
- Design, develop, and deploy advanced machine learning models for large-scale perception problems.
- Conduct research and empirical studies to evaluate new architectures, techniques, and algorithmic improvements, incorporating or adapting state-of-the-art methods as appropriate.
- Build and optimize deep learning architectures for object detection, segmentation, tracking, pose estimation, and scene understanding.
- Build and contribute to infrastructure and tools for supporting ML Pipeline to automate data labeling, training workflows, evaluation processes, and model versioning.
- Work extensively with large image, video, lidar and radar datasets to power next-generation computer vision systems.
What you bring
python
pytorch
cuda
computer vision
sensor fusion
computer science
- Proven track record of working autonomously and driving complex technical projects in fast-paced environments.
- Experience working directly with sensing hardware and understanding its constraints.
- Expertise in at least one deep learning framework such as PyTorch or TensorFlow.
- Experience with multi-modal perception (e.g., sensor fusion from cameras, lidar, radar).
- 4+ years of hands-on experience developing and deploying ML systems at scale.
- Strong background in computer vision and/or deep learning with practical experience in designing and training neural networks for real-world applications.
- Strong mathematical foundation in linear algebra, geometry, probability, and optimization.
- Bachelor’s or higher degree in Computer Science, Machine Learning, or a related technical discipline.
- After 30 Days: You have developed a deep understanding of the current perception architecture, sensor setup, and system requirements. You've identified key challenges in the ML pipelines and proposed initial areas for improvement across data workflows, model performance, and deployment constraints.
- Experience with GPU/TPU programming and optimization tools (e.g., CUDA, TensorRT).
- Publications in top-tier ML or CV conferences (e.g., CVPR, ICCV, NeurIPS, ICML, ECCV).
- After 60 Days: You’ve led the design of a new or improved perception subsystem and contributed hands-on to ML pipeline tooling. You've built a proof of concept aligned with system needs, demonstrating early improvements in performance or reliability based on real-world constraints.
- Background in autonomous systems, robotics, or other safety-critical domains.
- After 90 Days: You have delivered a perception feature with a proven working model in offline testing, showing measurable gains. The system is integrated into the pipeline and is progressing toward edge deployment, with a clear impact on overall perception capabilities.
- Excellent communication and collaboration skills, with experience working on interdisciplinary teams.
- Experience optimizing models for deployment on edge devices with real-time constraints.
- Proficiency in Python and familiarity with standard ML libraries and tools (e.g., NumPy, SciPy, Pandas).
- Knowledge of low-level programming languages like C++ or Rust.
Information not given or found
Training + DevelopmentInformation not given or found
Interview process
Information not given or found
Visa SponsorshipInformation not given or found
Security clearanceInformation not given or found
People also searched
Data Scientist Jobs in Los Angeles United StatesData Engineer Jobs in Los Angeles United StatesAI & ML Engineer Jobs in Los Angeles United StatesSoftware Engineer Jobs in Los Angeles United StatesData Scientist Jobs in Glendale United StatesData Engineer Jobs in Glendale United StatesAI & ML Engineer Jobs in Glendale United StatesSoftware Engineer Jobs in Glendale United States
Company
Overview
- Operates with a vision to revolutionize freight transportation using autonomy and sustainability.
- Focuses on developing electric, autonomous rail systems to reduce carbon emissions and improve efficiency.
- Has developed a cutting-edge platform for rail freight that minimizes human involvement and reduces operational costs.
- Has secured partnerships with logistics industry leaders to implement their autonomous systems for expansive use.
- Serves as a leader in the electrification of the rail sector through their innovative approach to freight transport.
- Their technology sets the stage for the future of autonomous rail logistics, streamlining transportation networks.
- Collaborative
- Autonomous
- Entrepreneurial
- Fast-paced
- People-first
- Progressive
- Supportive
- Mission-driven
- Data-driven
- Trust
- Originality
- Ownership
25% energy
Energy Efficiency
Autonomous battery-electric freight rail vehicles use just 25% of the energy compared to a semi-truck.
~33B gallons
Diesel Offset
Tech offsets approximately 33 billion gallons of diesel annually.
58,000 kg
Payload Capacity
Prototype can carry a payload of 58,000 kg with an 800 km range.
160-mile corridor
Pilot Corridor
Piloting zero-emissions railcars approved by FRA on a 160-mile Georgia corridor.
- Awarded $4.43M ARPA-E grant (Nov 3 2022–May 2 2025) to test energy performance and environmental impact.
- Tech offsets ~4.5% of all U.S. GHG emissions annually.
- DOE partnership includes analysis with NREL and RailTEC on energy efficiency.
- Piloting zero-emissions railcars aiming for commercialization by 2026.
- Founded and staffed by professionals with backgrounds at SpaceX, Tesla, Google and Uber
- Team described as diverse mission-driven professionals, fostering a collaborative culture of inclusion, trust and originality
- No publicly disclosed gender breakdown or specific gender-related statistics available
Articles
Kablio AIIf you're someone who helps build and power the world (or dreams to), Kablio AI is your pocket-sized recruiter that gets you hired.
Copyright © 2025 Kablio