Machine Learning/Computer Vision Engineer
Parallel
•
Los Angeles, CA
0
0
0
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Entry level
Posted April 5, 2026
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Responsibilities
Commitments
Responsibilities
- Training and improving deep learning models for perception tasks including detection, segmentation, tracking, and scene understanding
- Developing scalable training pipelines for large perception datasets
- Working with multimodal sensor data including cameras, lidar, and radar
- Evaluating and adapting state-of-the-art research for real-world deployment
- Improving inference performance and model reliability under real-world conditions
- Supporting deployment of perception models to edge compute systems on autonomous vehicles What Success Looks Like
- After 30 Days : You understand the fundamentals of Parallel’s perception stack, datasets, and model training workflows.
- You are contributing to model training experiments and supporting improvements to the ML pipeline.
- After 60 Days: You are independently training and evaluating models for a defined perception task and contributing improvements to model performance or data workflows.
- After 90 Days: You have contributed a measurable improvement to an existing perception model or training pipeline and are actively supporting deployment and testing efforts.
Commitments
Work on real autonomous systems operating in the physical world
The target hiring range for this position is listed below.
As part of this commitment, Parallel Systems will ensure that persons with disabilities are provided reasonable accommodations.
If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact your recruiter.
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What still needs stronger evidence
Requirements
- 1 to 2 years of experience building machine learning systems or exceptional new graduates with strong internships or research experience
- Experience developing machine learning models using PyTorch or TensorFlow
- Strong programming ability in Python
- Solid mathematical foundation in linear algebra, probability, and optimization
- Experience working with real-world datasets in computer vision, robotics, or machine learning Preferred Qualifications
- Internship experience at leading technology, robotics, autonomy, or AI companies
- Research experience in machine learning, computer vision, or robotics labs
- Experience with perception tasks such as object detection, segmentation, tracking, or 3D vision
- Experience working with image, video, lidar, or radar datasets
- Familiarity with training pipelines, dataset tooling, and model evaluation
- Experience with C++, CUDA, or GPU optimization
- Publications or academic research in ML/CV venues such as CVPR, ICCV, ECCV, NeurIPS, or ICML What Makes This Role Unique
- Work on real autonomous systems operating in the physical world
- Build perception systems that must perform reliably in safety-critical environments
- Collaborate with engineers across autonomy, robotics, and systems engineering
Preferred Skills
- Experience working with real-world datasets in computer vision, robotics, or machine learning Preferred Qualifications
- Research experience in machine learning, computer vision, or robotics labs
- Experience with perception tasks such as object detection, segmentation, tracking, or 3D vision
- Experience with C++, CUDA, or GPU optimization
- Publications or academic research in ML/CV venues such as CVPR, ICCV, ECCV, NeurIPS, or ICML What Makes This Role Unique
- Work on real autonomous systems operating in the physical world
- Build perception systems that must perform reliably in safety-critical environments
- Collaborate with engineers across autonomy, robotics, and systems engineering
Education
- (Not required) – Bachelor’s or Master’s degree in Computer Science, Robotics, Machine Learning, Electrical Engineering, or a closely related technical field
- (Not required) – 1 to 2 years of experience building machine learning systems or exceptional new graduates with strong internships or research experience
- (Not required) – Solid mathematical foundation in linear algebra, probability, and optimization
Parallel Systems is pioneering autonomous battery-electric rail vehicles designed to transform freight transportation by shifting portions of the $900 billion U.S. trucking industry onto rail. Our innovative technology offers cleaner, safer, and more efficient logistics solutions. Join our dynamic team and help shape a smarter, greener future for global freight. Machine Learning / Computer Vision Engineer Perception Systems Parallel Systems is building autonomous, battery-electric rail vehicles designed to transform freight transportation by shifting portions of the $900B trucking industry onto rail. Our vehicles operate on active rail networks and must perceive and reason about complex real-world environments with extremely high reliability. We are looking for an early-career Machine Learning / Computer Vision Engineer to help develop the perception systems that power our autonomous platform. You will work on real-world computer vision problems involving multi-sensor data, large-scale datasets, and real-time inference constraints. This role is ideal for engineers who have strong foundations in machine learning and computer vision and who have already built meaningful ML systems through research, internships, or early industry experience. You will work closely with experienced engineers across autonomy, robotics, and systems to design and deploy perception models that operate on real vehicles. What You’ll Work On
Training and improving deep learning models for perception tasks including detection, segmentation, tracking, and scene understanding
Developing scalable training pipelines for large perception datasets
Working with multimodal sensor data including cameras, lidar, and radar
Evaluating and adapting state-of-the-art research for real-world deployment
Improving inference performance and model reliability under real-world conditions
Supporting deployment of perception models to edge compute systems on autonomous vehicles What Success Looks Like
After 30 Days : You understand the fundamentals of Parallel’s perception stack, datasets, and model training workflows. You are contributing to model training experiments and supporting improvements to the ML pipeline.
After 60 Days: You are independently training and evaluating models for a defined perception task and contributing improvements to model performance or data workflows.
After 90 Days: You have contributed a measurable improvement to an existing perception model or training pipeline and are actively supporting deployment and testing efforts. Basic Qualifications
Bachelor’s or Master’s degree in Computer Science, Robotics, Machine Learning, Electrical Engineering, or a closely related technical field
1 to 2 years of experience building machine learning systems or exceptional new graduates with strong internships or research experience
Experience developing machine learning models using PyTorch or TensorFlow
Strong programming ability in Python
Solid mathematical foundation in linear algebra, probability, and optimization
Experience working with real-world datasets in computer vision, robotics, or machine learning Preferred Qualifications
Internship experience at leading technology, robotics, autonomy, or AI companies
Research experience in machine learning, computer vision, or robotics labs
Experience with perception tasks such as object detection, segmentation, tracking, or 3D vision
Experience working with image, video, lidar, or radar datasets
Familiarity with training pipelines, dataset tooling, and model evaluation
Experience with C++, CUDA, or GPU optimization
Publications or academic research in ML/CV venues such as CVPR, ICCV, ECCV, NeurIPS, or ICML What Makes This Role Unique
Work on real autonomous systems operating in the physical world
Build perception systems that must perform reliably in safety-critical environments
Collaborate with engineers across autonomy, robotics, and systems engineering
Contribute directly to a new class of autonomous transportation platform We are committed to providing fair and transparent compensation in accordance with applicable laws. Salary ranges are listed below and reflect the expected range for new hires in this role, based on factors such as skills, experience, qualifications, and location. Final compensation may vary and will be determined during the interview process. The target hiring range for this position is listed below. Target Salary Range:: $120,000 USD - $190,000 USD Parallel Systems is an equal opportunity employer committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regard to any discriminatory factor protected by applicable federal, state or local laws. We work to build an inclusive environment in which all people can come to do their best work. Parallel Systems is committed to the full inclusion of all qualified individuals. As part of this commitment, Parallel Systems will ensure that persons with disabilities are provided reasonable accommodations. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact your recruiter.
Training and improving deep learning models for perception tasks including detection, segmentation, tracking, and scene understanding
Developing scalable training pipelines for large perception datasets
Working with multimodal sensor data including cameras, lidar, and radar
Evaluating and adapting state-of-the-art research for real-world deployment
Improving inference performance and model reliability under real-world conditions
Supporting deployment of perception models to edge compute systems on autonomous vehicles What Success Looks Like
After 30 Days : You understand the fundamentals of Parallel’s perception stack, datasets, and model training workflows. You are contributing to model training experiments and supporting improvements to the ML pipeline.
After 60 Days: You are independently training and evaluating models for a defined perception task and contributing improvements to model performance or data workflows.
After 90 Days: You have contributed a measurable improvement to an existing perception model or training pipeline and are actively supporting deployment and testing efforts. Basic Qualifications
Bachelor’s or Master’s degree in Computer Science, Robotics, Machine Learning, Electrical Engineering, or a closely related technical field
1 to 2 years of experience building machine learning systems or exceptional new graduates with strong internships or research experience
Experience developing machine learning models using PyTorch or TensorFlow
Strong programming ability in Python
Solid mathematical foundation in linear algebra, probability, and optimization
Experience working with real-world datasets in computer vision, robotics, or machine learning Preferred Qualifications
Internship experience at leading technology, robotics, autonomy, or AI companies
Research experience in machine learning, computer vision, or robotics labs
Experience with perception tasks such as object detection, segmentation, tracking, or 3D vision
Experience working with image, video, lidar, or radar datasets
Familiarity with training pipelines, dataset tooling, and model evaluation
Experience with C++, CUDA, or GPU optimization
Publications or academic research in ML/CV venues such as CVPR, ICCV, ECCV, NeurIPS, or ICML What Makes This Role Unique
Work on real autonomous systems operating in the physical world
Build perception systems that must perform reliably in safety-critical environments
Collaborate with engineers across autonomy, robotics, and systems engineering
Contribute directly to a new class of autonomous transportation platform We are committed to providing fair and transparent compensation in accordance with applicable laws. Salary ranges are listed below and reflect the expected range for new hires in this role, based on factors such as skills, experience, qualifications, and location. Final compensation may vary and will be determined during the interview process. The target hiring range for this position is listed below. Target Salary Range:: $120,000 USD - $190,000 USD Parallel Systems is an equal opportunity employer committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regard to any discriminatory factor protected by applicable federal, state or local laws. We work to build an inclusive environment in which all people can come to do their best work. Parallel Systems is committed to the full inclusion of all qualified individuals. As part of this commitment, Parallel Systems will ensure that persons with disabilities are provided reasonable accommodations. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact your recruiter.