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About This Role
Overview:
The Senior AI Engineer will design, develop, and deploy artificial intelligence and machine learning solutions that support Panasonic Automotive’s digital transformation, engineering productivity, and business innovation initiatives. This role will partner with cross\-functional teams to build scalable AI\-enabled applications, integrate enterprise data sources, and promote responsible, secure, and effective use of AI technologies.
Responsibilities:
A DAY IN THE LIFE:
- Design, develop, and deploy AI and machine learning solutions that address business and engineering use cases.
- Build and optimize generative AI applications, including chatbots, document intelligence tools, workflow automation, and knowledge retrieval solutions.
- Develop prompts, agents, APIs, and integrations that connect AI capabilities with enterprise systems and data sources.
- Collaborate with engineering, IT, cybersecurity, data, and business teams to identify opportunities for AI\-driven improvements.
- Evaluate, test, and implement large language models, machine learning models, and AI frameworks based on business requirements.
- Create scalable and maintainable AI pipelines, services, and applications using modern software engineering practices.
- Monitor AI solution performance, accuracy, security, cost, and user adoption.
- Support governance, responsible AI practices, and compliance requirements for enterprise AI usage.
- Document technical designs, deployment steps, operating procedures, and support guidelines.
- Mentor team members and share best practices for AI engineering, automation, and software development.
MUST HAVES:
- Bachelor’s degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related technical field.
- 5\+ years of professional software engineering, data engineering, machine learning engineering, AI
- Hands\-on experience developing AI or machine learning solutions using Python, APIs, cloud services, and modern development frameworks.
- Experience working with generative AI, large language models, retrieval\-augmented generation, prompt engineering, embeddings, or vector databases.
- Experience designing and deploying scalable applications, services, or automation workflows.
- Strong understanding of software development lifecycle practices, including version control, testing, documentation, CI/CD, and production support
- Ability to translate business requirements into technical designs and working solutions.
- Strong analytical, problem\-solving, communication, and collaboration skills.
- Ability to work effectively with cross\-functional teams in a fast\-paced environment.
- Familiarity with data privacy, cybersecurity, access control, and responsible AI principles.
BENEFITS \& PERKS \- WE'RE ALL ABOUT YOU:
- Great Medical/Dental Benefits
- Company\-Matched 401K Retirement Savings
- Annual Bonus Program
- Educational Assistance
- Relaxed Dress Code
- PASATalks Speaker Summits
- Leadership \& Mentorship Programs
- High5 Reward Recognition Program
- Onsite Happy Hours
- And many more benefits \& perks found within the ‘Our Culture’ section…
WHO WE ARE:
At Panasonic, our technology and engineering expertise delivers innovation across diverse industries. It's all about the consumer experience and making sure that we find ways to enhance that experience, either through audio enhancements or through safety enhancements inside the vehicle.
Panasonic Automotive Systems Company of America (PASA) is an industry\-leading global supplier to Automotive Original Equipment Manufacturers (OEM’s) for infotainment systems and advanced connected car solutions. Our clients include Ford, GM, Chrysler, Daimler, Fiat, Tesla, Honda, Toyota.
WE TAKE OPPORTUNITY SERIOUSLY:
Panasonic is an Equal Opportunity/Affirmative Action employer, and all qualified applicants will receive consideration for employment without regard to: race, color, religion, sex, sexual orientation, gender identity, national origin, age, genetic information, disability status, protected veteran status, or any other characteristics protected by law. All qualified individuals are required to perform the essential functions of the job with our without reasonable accommodations.
Due to the high volume of responses, we will only be able to respond to candidates of interest. All candidates must have valid authorization to work in the U.S. Thank you for your interest in Panasonic Automotive Systems of America.
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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 3,057 AI roles we're tracking, AI/ML Engineer positions make up 72% of the market. At Panasonic Automotive, 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 $179,000 based on 11,905 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Panasonic Automotive AI Hiring
Panasonic Automotive has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Farmington Hills, MI, US.
Location Context
Across all AI roles, 17% (513 positions) offer remote work, while 2,528 require on-site attendance. Top AI hiring metros: New York (2,449 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,057 open positions tracked in our dataset. By seniority: 94 entry-level, 1,467 mid-level, 1,148 senior, and 348 leadership roles (Director, VP, C-Level). Remote roles make up 17% of the market (513 positions). The remaining 2,528 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,057 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,189), Data Scientist (233), AI Software Engineer (195). 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 (94) are outnumbered by mid-level (1,467) and senior (1,148) 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 348 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 17% of all AI roles (513 positions), with 2,528 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,566 postings), Aws (974 postings), Azure (725 postings), Rag (683 postings), Gcp (597 postings), Prompt Engineering (472 postings), Pytorch (461 postings), Claude (447 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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