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About This Role
Overview:
Do you want to join a team that's changing the world? Do you have a strong background as a Sr. Engineer, Industrial AI and Analytics? Then we're looking for you! Check out the job description and apply now! Put your skills to meaningful use, gain unique experience, and work with world\-class team members with diverse backgrounds and expertise who share the same vision. Join the PECNA team today!
Responsibilities:
Sr. Engineer, Industrial AI and Analytics
https://www.youtube.com/watch?v\=0tMgKm\_71qs
(*by clicking this link you are being referred to an external site that is not part of Panasonic*) Meet the Recruiter:Princess DamatoJob Summary:
Panasonic Energy is seeking a Sr. Engineer, Industrial AI and Analytics reporting to the Sr. Director, Next Gen Initiatives. This role operates within the Next Gen Initiatives (NGI) team, a strategic function responsible for advancing automation, AI enablement, and operational transformation across PECNA. This role focuses on the digital and systems execution layer of manufacturing: deploying analytics, industrial AI, data products, and decision\-support tools that improve how manufacturing teams see, decide, and act. The engineer will drive deployment of factory intelligence platforms, operational dashboards, AI/ML solutions, digital twin/simulation concepts, command\-center capabilities, and AI\-enabled decision support. This role converts Next Gen concepts into production\-ready digital capabilities that improve safety, quality, productivity, throughput, and cost performance. The ideal candidate blends future\-forward curiosity with a hands\-on approach to problem\-solving. You will translate complex manufacturing challenges into structured initiatives, driving cross\-functional execution from early discovery and ideation through proof\-of\-concept and full deployment, ensuring measurable performance improvements and sustained adoption across the shop floor.Essential Duties:
Strategy \& Execution* Partner with site leadership and manufacturing stakeholders to define and prioritize analytics, digital AI, and production systems initiatives aligned to NGI pillars and identify opportunities to improve performance, cost, and workforce capability.
- Translate operational challenges into clear initiative charters and drive end\-to\-end delivery across digital systems (analytics/AI).
- Own use cases from discovery through proof of concept to scaled deployment, ensuring solutions are operationally relevant, explainable, and embedded into frontline workflows.
- Manage initiative timelines, risks, and dependencies; drive issue resolution with cross\-functional stakeholders.
Next Gen Technologies* Evaluate, pilot, deploy emerging analytics and digital AI technologies across production sites, including industrial data platforms, AI/ML solutions, digital twins, command centers, and agentic/GenAI tools. Identify and evaluate high\-impact opportunities in areas such as predictive maintenance, quality prediction, SPC automation, process optimization, yield improvement, and decision support.
- Identify and prioritize analytics and digital AI opportunities that deliver measurable improvements in productivity, throughput, quality, and cost performance.
- Partner with Operations, Process Engineering, Quality, Maintenance, IT/OT, SCADA/MES, and data teams to convert manufacturing data into reliable, governed data products, dashboards, and decision\-support tools.
- Develop evaluation frameworks to assess readiness, data quality, usability, security, reliability, scalability, and ROI before scaled deployment.
Stakeholder \& Vendor Coordination* Bridge the gap between Operations, Engineering, Safety, Maintenance, DataX, Quality, Finance, HR, and Supply Chain to ensure digital deployments are secure, scalable, adopted, and sustainable.
- Drive deployment of Nevada tools to Kansas.
- Manage relationships with data/AI platform partners, systems integrators, and analytics vendors, including requirements, delivery milestones, validation, and performance accountability.
- Define and track KPIs such as data quality, adoption, uptime/availability, throughput, yield/quality, cost performance, workflow efficiency, and ROI.
- Document standards, data definitions, workflows, and best practices to enable repeatable digital deployment across NV and KS sites.
- Support training and change management for sustainable ownership by relevant teams.
Personal Protective Equipment (PPE) Requirements:* To ensure health and safety in the workplace and for employee protection, wearing PPE is required and includes equipment such as a full Tyvek suit, safety shoes, gloves, safety glasses, face mask, and a full hazmat suit that includes a respirator. A respirator fit test will be required based on functional area.
*The foregoing description is not intended and should not be construed to be an exhaustive list of all responsibilities, skills and efforts or work conditions associated with the job. It is intended to be an accurate reflection of the general nature and level of the job.*
Qualifications:
Requirements \- Required and/or PreferredRequired Education, Certifications, and Licenses:* Bachelor’s degree in Engineering, Data Science, Industrial Engineering, Computer Science, Information Systems, or related field
Preferred Education, Certifications, and Licenses:* Master’s degree in a related field
- PMP, Palantir Foundry certification, or equivalent continuous improvement / digital platform certification
Essential Qualifications:* 7\+ years of experience in manufacturing analytics, industrial data systems, production systems, digital transformation, applied AI/ML, or systems integration within a high\-volume manufacturing environment. Proven cross\-functional delivery from concept to adoption.
- Knowledge of factory systems (MES, SCADA, PLCs, ERP, CMMS, SPC) and how they interconnect to support analytics and decision\-making.
- Experience building or managing delivery of operational dashboards, data pipelines, data products, analytics applications, or AI\-enabled tools. Strong communication, structured problem\-solving, and stakeholder management skills.
- Strong project execution skills with ability to manage scope, timeline, risks, dependencies, adoption, and cross\-functional stakeholders.
- Strong communication skills, comfortable working on the shop floor with operations and presenting progress to leadership.
Preferred Qualifications:* Experience in battery, semiconductor, automotive, pharmaceutical, or high\-speed consumer goods manufacturing
- Experience with one or more programming, scripting, or data development languages such as Python, SQL, JavaScript, C\#, Java, R, or similar tools used for analytics, automation, application development, or systems integration.
- Experience with industrial analytics platforms, Palantir Foundry or similar tools, operational dashboards, digital twins, predictive maintenance, quality analytics, or command\-center capabilities
- Hands\-on experience using modern large language models and GenAI tools such as Claude, Gemini, ChatGPT, Microsoft Copilot, or similar platforms to improve productivity, automate workflows, support data analysis, or develop AI\-enabled business and manufacturing solutions.
- Familiarity with industrial data architecture, Industrial Data Fabric, Unified Namespace, OT cybersecurity, OPC\-UA/DA, MQTT, Modbus, or related data integration concepts
- Experience with Lean, Six Sigma, TPM, analytics adoption, data governance, or continuous improvement methodologies
- Experience working with Japanese manufacturing partners or in cross\-cultural engineering environments
Travel Requirement:* Up to 25% travel required
Physical Demands:
Physical Activities: Percentage of time (equaling 100%) during the normal workday the employee is required to:* Sit: 50%
- Walk: 30%
- Stand: 20%
Required Lifting and Carrying: *Rare (\<1%), Occasional (1\-33%), Frequent (34\-66%), Continuous (67\-100%)*
For this position, the required frequency is:* Up to 10 lbs.: Occasional
- Up to 20 lbs.: Rare
- Up to 35 lbs.: Rare
- Team\-lift only (over 35 lbs.): Rare
Benefits \& Perks \- What's In It For You:
Panasonic Energy prioritizes total well\-being and offers comprehensive benefits options to support physical, emotional, financial, social, and environmental health:* Health Benefits – Offering medical, dental, vision, prescription plans, plus Health Savings Account and Flexible Spending Account options.
- Voluntary Benefits – Life, accident, critical illness, disability, legal, identity theft, and pet insurance.
- Panasonic Retirement Savings \& Investment Plan (PRSIP) – 401(k) plan with company matching contributions and immediate vesting.
- Paid Time\-Off Benefits – Vacation, holidays, personal days, sick leave, volunteer, and parental \& caregiver leave.
- Educational Assistance – Tuition reimbursement for job\-related courses after six months of service.
- Health Management and Wellbeing Programs –Lifestyle Spending Account, EAP, virtual health management, chronic condition, neurodiversity, tobacco cessation, substance abuse support, and life stage and fertility resources. Available to eligible employees starting the first day of the month following your start date. Eligibility for each benefit may vary based on employment status, location, and length of service.
- Employee Recognition Program \- High5 employee recognition and awards platform, quarterly and annual employee recognition
- Annual Bonus Program \- Opportunity for an annual performance\-based bonus.
- On\-site Food Options: Several on\-site cafes, plentiful snack and beverage kitchens, revolving on\-site vendor visits and employee events
- Free Shuttle: Rides to and from work!
Where You'll Be:
For our onsite roles, Panasonic Energy is committed to fostering an ideal working environment that goes beyond the conventional. We understand the significance of moments that matter in your onsite experience, and we prioritize creating a workspace that not only promotes productivity but also ensures a fulfilling and positive work atmosphere. Join us at Panasonic Energy, where your onsite presence is valued, and we strive to make each moment count in your professional journey.Who We Are:
Meet Panasonic Energy! At Panasonic Energy, you'll do work that matters as we are dedicated to transforming the world through the acceleration of sustainable energy. By producing safe, high\-quality lithium\-ion batteries, you become part of a team that plays a crucial role in creating technologies that move us.
Our journey began in 2017, and now, as the world's largest lithium\-ion battery factory, we are expanding operations to De Soto, Kansas, providing you with the opportunity to experience career growth in more ways than one.
As an innovative thinker, you'll thrive here, as we continually push the boundaries of lithium\-ion battery technology and production capabilities to enhance efficiency and performance in EVs.
Being part of Panasonic Energy means positively contributing to society, aligning with our commitment to building a better world through sustainable energy solutions.
We care about what you care about, fostering an environment where your contributions make a meaningful impact on the future of energy and transportation. Join us and be part of a team that values your work, encourages innovation, and actively contributes to a positive societal impact.
In addition to an environment that is as innovative as our products, we offer competitive salaries and benefits.We Take Opportunity Seriously:
At Panasonic Energy, we are committed to a workplace that genuinely fosters inclusion and belonging. Fairness and Honesty have been part of our core values for more than 100 years and we are proud of our diverse culture as an equal opportunity employer.
We understand that your career search may look different than others and embrace the professional, personal, educational, and volunteer opportunities through which people gain experience. If you are actively looking or starting to explore new opportunities, submit your application!Supplemental Information:
Pre\-employment drug testing is required.
Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status or other characteristics protected by law. All qualified individuals are required to perform the essential functions of the job with or without reasonable accommodation.*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. without restriction.*
Thank you for your interest in Panasonic Energy Corporation of North America.
\#LI\-PD1
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Panasonic, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Panasonic AI Hiring
Panasonic has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Sparks, NV, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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