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About This Role
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What we offer:
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At Magna, you can expect an engaging and dynamic environment where you can help to develop industry\-leading automotive technologies. We invest in our employees, providing them with the support and resources they need to succeed. As a member of our global team, you can expect exciting, varied responsibilities as well as a wide range of development prospects. Because we believe that your career path should be as unique as you are.Group Summary:
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Transforming mobility. Making automotive technology that is smarter, cleaner, safer and lighter. That’s what we’re passionate about at Magna Electronics, and we do it by creating world\-class Electronic systems. We are a premier supplier for the global automotive industry with full capabilities in design, development, testing and manufacturing of complex Electronic systems. Our name stands for quality, environmental consciousness, and safety. Innovation is what drives us and we drive innovation. Dream big and create the future of mobility at Magna Electronics.Job Responsibilities:
This role is not eligible for visa sponsorship. Candidates must have current and ongoing authorization to work in the United States.
JOB SUMMARY
The Computer Vision Algorithm Engineer role focused on ADAS perception that turns camera video feeds (image frames) into a clear understanding of the vehicle’s surroundings. The work spans concept through serial production: design and simulate algorithms, analyze and replay data, build and test classical and deep learning models, and optimize for real\-time execution on production ECUs. Core tasks include object detection, segmentation, tracking, and image enhancement while meeting accuracy, latency, memory, and power targets. A strong background in image processing, machine learning, and mathematics/physics is required, with familiarity in vehicle dynamics considered a plus.
ESSENTIAL JOB FUNCTIONS
- Develop (design, implement, optimize) conventional image processing algorithms for automotive embedded serial production projects.
- Design, develop/tune, and optimize deep learning models for ADAS computer vision features (e.g., pruning, quantization) and improve computational performance.
- Plan and execute experiments to assess deep learning model effectiveness, compare architectures, and validate results through rigorous component/bench testing.
- Strong knowledge of various camera models; lens distortion correction; homograph and projective transformations mathematical techniques
- Analyze large datasets to extract insights, refine models, and improve overall performance and robustness.
- Stay current with deep learning advances and incorporate innovative techniques and research findings into projects.
- Collaborate with multidisciplinary (requirements, embedded, testing) teams to integrate models into existing systems and ensure seamless operation within the product ecosystem.
- Document development processes, maintain detailed experiment logs, and present findings clearly to stakeholders.
- Analyze defects and test results; perform root\-cause analysis and implement algorithm improvements to achieve KPIs.
- Independently deliver intermediate\-to\-advanced ADAS algorithm design, implementation, and testing.
- Perform other duties in support of business objectives; maintain regular attendance; follow safe work procedures and PPE requirements; report hazards, injuries, and illnesses promptly; comply with Quality Operating System (QOS) and all safety regulations.
JOB REQUIREMENTS
*Education/Experience*
- Master’s degree in computer engineering, Software Engineering, Electrical Engineering, Computer Science, or equivalent.
- Minimum of 3 years of experience in computer vision and image‑processing algorithm development using traditional methods and deep learning, with proven expertise in developing and implementing DNN models.
- Excellent programming skills with C or C\+\+; familiarity with Python with proficiency in deep learning frameworks (TensorFlow, PyTorch, Keras) is advantageous.
- Strong grasp of machine learning concepts and neural network architectures (CNNs, RNNs, transformers).
- Experience in image segmentation, object detection, and image data preparation/enhancement (e.g., normalization, augmentation, filtering, noise reduction, contrast adjustment, image restoration).
- Experience in optimizing models for performance, including techniques such as quantization and distributed training.
- Strong problem\-solving abilities; capable of working independently and collaboratively; effective at communicating complex concepts to technical and non\-technical audiences.
- Experience with responsibilities listed above in the serial development of automotive electronics is preferred.
*Technical Knowledge*
- Strong foundations in mathematics and signal/image/video processing; computer vision fundamentals (object detection, tracking, feature extraction) with C/C\+\+.
- Experience developing AI and Machine Learning Algorithms for embedded devices.
- Knowledge of automotive product development practices and structured engineering methodologies; development of portable, reusable, modular software for automotive systems.
- Strong troubleshooting and debugging skills, using structured problem\-solving methods (e.g., 8D).
- Experience with disciplined software development processes (ASPICE or CMMI); configuration management; and project monitoring/control techniques.
*Personal Requirements*
- Able to work effectively in a global environment
- Able to represent technical topics internally and externally
- Demonstrates self‑motivation, tenacity, and determination.
- Able to work independently with minimal supervision.
- Comprehensive knowledge of English (speak \& write)
PHYSICAL DEMANDS
Normal amount of sitting and standing, average mobility to move around an office and plant environment, able to conduct normal amount of work on a computer.
LIMITATIONS AND DISCLAIMER
The above job description is meant to describe the general nature and level of work being performed; it is not intended to be construed as an exhaustive list of all responsibilities, duties and skills required for the position. Requirements are representative of minimum levels of knowledge, skills and/or abilities. To perform this job successfully, the employee must possess the abilities or aptitudes to perform each duty proficiently.
All job requirements are subject to possible modification to reasonably accommodate individuals with disabilities. Some requirements may exclude individuals who pose a direct threat or significant risk to the health and safety of themselves or other employees.
This job description in no way states or implies that these are the only duties to be performed by the employee occupying this position. Employees will be required to follow any other job\-related instructions and to perform other job\-related duties as requested by their supervisor in compliance with federal and state laws.
Awareness, Unity, Empowerment:
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At Magna, we believe that a diverse workforce is critical to our success. That’s why we are proud to be an equal opportunity employer. We hire on the basis of experience and qualifications, and in consideration of job requirements, regardless of, in particular, color, ancestry, religion, gender, origin, sexual orientation, age, citizenship, marital status, disability or gender identity. Magna takes the privacy of your personal information seriously. We discourage you from sending applications via email or traditional mail to comply with GDPR requirements and your local Data Privacy Law.
AI\-Assisted Screening Disclosure
As part of our commitment to a fair, consistent, and efficient recruitment process, we may use artificial intelligence (AI) tools to assist in the initial screening of applications submitted through our Workday system. These tools help identify qualifications and experience that align with the role requirements. Please note that AI is used solely to support our recruiters. Final decisions are always made by the hiring manager and the hiring team. Importantly, no applicant data is shared externally through these AI tools. All information remains securely within our systems and is handled in accordance with our privacy and data protection policies.
Under conditions defined by applicable law, you may have the right to request an explanation of how AI is used to support decision\-making.
If you have any questions or concerns about this process, feel free to contact our Talent Attraction team.
Worker Type:
Regular / PermanentGroup:
Magna Electronics
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Magna International, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Magna International AI Hiring
Magna International has 2 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer. Positions span Auburn Hills, MI, US, Troy, MI, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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