Physical AI Senior Manager
$120K - $159K/yr
Hands-on experience with NVIDIA ecosystem elements relevant to Physical AI (e.g., accelerated ... cybersecurity, model drift, human-in-the-loop workflows, and maintenance/operating model ...
Physical AI Senior Manager
$120K - $159K/yr
Hands-on experience with NVIDIA ecosystem elements relevant to Physical AI (e.g., accelerated ... cybersecurity, model drift, human-in-the-loop workflows, and maintenance/operating model ...
Nvidia Cybersecurity information
Does NVIDIA have cyber security jobs?
NVIDIA offers cybersecurity jobs that involve protecting its hardware, software, and data from cyber threats. These roles often require knowledge of network security, threat analysis, and security tools, and may require relevant certifications such as CISSP or CEH. Cybersecurity positions at NVIDIA typically involve collaboration with engineering teams and adherence to industry security standards.
What are the key skills and qualifications needed to thrive in the Nvidia Cybersecurity position, and why are they important?
To succeed in Nvidia Cybersecurity, candidates typically require a strong background in information security, network defense, and risk assessment, often backed by a degree in computer science or a related field. Familiarity with cybersecurity tools such as SIEMs, IDS/IPS, and certifications like CISSP or CEH are highly valued. Analytical thinking, strong problem-solving abilities, and effective communication help individuals excel in cross-functional environments. These competencies ensure the protection of sensitive data, rapid response to threats, and seamless teamwork in a fast-paced technology landscape.
Can you make $500,000 a year in cyber security?
Cybersecurity professionals, including those working in roles like cybersecurity engineers or senior analysts, can potentially earn $500,000 annually with extensive experience, advanced certifications, and leadership responsibilities. Such high salaries are typically found in senior or executive positions, often in large organizations or specialized consulting roles. Entry- and mid-level positions usually have lower compensation levels.
How much does a cyber security analyst make at NVIDIA?
A cybersecurity analyst at NVIDIA typically earns between $80,000 and $120,000 annually, depending on experience, location, and certifications. The role often requires knowledge of security tools, network protocols, and threat detection techniques.
What does a typical workday look like for someone in Nvidia Cybersecurity?
A typical workday in Nvidia Cybersecurity involves monitoring security alerts, investigating potential threats, and collaborating with IT, engineering, and security teams to implement protective measures. Professionals frequently conduct vulnerability assessments, review system logs, and participate in incident response activities to ensure the organization's assets are secure. They may also help develop security protocols and provide guidance during code reviews. This dynamic environment offers continuous learning and a chance to work on cutting-edge technology alongside industry experts.
What is a Nvidia Cybersecurity job?
An Nvidia Cybersecurity job involves protecting the company's systems, data, and infrastructure from cyber threats. Professionals in this role work on threat detection, risk assessment, vulnerability management, and incident response. They collaborate with IT and security teams to develop and implement security policies, ensuring compliance with industry standards. Additionally, they may use AI-driven security solutions to enhance threat analysis and mitigation.
Is it hard to get hired by NVIDIA?
Getting hired for a cybersecurity role at NVIDIA can be competitive, as the company seeks candidates with strong technical skills, relevant experience, and often advanced certifications. The hiring process typically involves multiple interviews, technical assessments, and a review of qualifications, making it important to have a solid background in cybersecurity tools and practices.
What are the most commonly searched types of Nvidia Cybersecurity jobs in Indiana? The most popular types of Nvidia Cybersecurity jobs in Indiana are:
What are popular job titles related to Nvidia Cybersecurity jobs in Indiana? For Nvidia Cybersecurity jobs in Indiana, the most frequently searched job titles are:
What job categories do people searching Nvidia Cybersecurity jobs in Indiana look for? The top searched job categories for Nvidia Cybersecurity jobs in Indiana are:
What cities in Indiana are hiring for Nvidia Cybersecurity jobs? Cities in Indiana with the most Nvidia Cybersecurity job openings:
Deloitte rating
8.0
Based on 89 frontline employees who took The Breakroom Quiz
55th of 139 rated financial services
Job description
Physical AI Senior Manager - Manufacturing & Supply Chain
We are a team of strategic advisors, architects, and implementers who drive business transformations. Our diverse talent energizes clients' business functions and technology to maximize value in Supply Chain enhancing their ability to fulfill their growth and efficiency ambitions. Imagine working with world-class supply network capabilities like Smart Factory, Strategy & Innovation, Supply Chain Responsiveness, Sourcing & Procurement, or Product Development & Operations!
Are you ready to take your career to new heights? Join our US Supply Chain & Network Operations Offering, where you'll deliver transformational solutions using operational expertise, digital technologies, advanced analytics, and industry-specific hybrid solutions. Don't miss the chance to be part of a team that provides exceptional client value while advancing your professional journey. Apply now and become a vital part of our innovative and dynamic workforce!
Recruiting for this role ends on 8/31/26.
The team
You will join a cross-functional Supply Chain & Manufacturing consulting environment focused on helping clients modernize operations through technology, data, and advanced engineering. The role operates in a matrix of industry practitioners, technologists, and alliance partners and requires strong collaboration, structured problem-solving, and the ability to translate emerging technology into operational results.
Work you'll do
You will serve as the functional and domain expert for Physical AI-where AI meets the physical world-across manufacturing and supply chain operations. You will shape advisory engagements, lead proofs of concept (PoCs), and drive implementation programs that combine robotics, computer vision, simulation, digital twins, synthetic data, and edge AI, frequently in partnership with ecosystem alliance providers (e.g., NVIDIA, Siemens, AWS and others). Candidates should be comfortable in factories, warehouses, and leadership conference rooms with the experience to translate between controls engineers, data scientists, and frontline operations.
Key responsibilities- Lead Physical AI strategy and advisory for manufacturing and supply chain clients. Identify high-value use cases (e.g., quality inspection, safety, intralogistics, material handling, asset monitoring, autonomous operations), define value hypotheses, and translate to roadmaps and business cases.
- Own solution shaping and end-to-end architecture spanning sensors, vision, data pipelines, model development, simulation, edge deployment, and operations (i.e., MLOps and ModelOps), with explicit acceptance criteria for operational environments.
- Drive PoCs and pilots to measurable outcomes. Define experiments, data collection plans, synthetic data approaches (when appropriate), evaluation metrics, and scale plans from pilot-to-plant and factory/network rollout.
- Integrate AI with real-world constraints: latency, reliability, safety, OT/IT connectivity, cybersecurity, model drift, human-in-the-loop workflows, and maintenance/operating model considerations.
- Partner with alliances and product teams to translate partner platforms into client-ready reference architectures, demos, and repeatable delivery assets.
- Influence pursuits and proposals: support scoping, estimating, staffing, risk and assumption framing, and executive-level storytelling. Serve as technical authority in client workshops and due diligence.
- Lead and mentor multi-disciplinary teams: data science, ML engineering, software and edge, vision, robotics and controls, manufacturing experts, and contribute to capability-building and market activation.
The team
Qualifications
Required
- Bachelor's degree or equivalent practical experience.
- 10+ years of relevant experience, including client leadership, team leadership, and sustained contribution to business development/pursuits.
- Experience in at least two of the following domains:
- Computer vision for industrial environments (e.g., inspection, defect detection, safety, tracking, manufacturing assembly)
- Robotics and autonomy (e.g., industrial robotics, mobile robotics and AMRs, perception-to-action workflows)
- Simulation, digital twins, physics-based modeling for factories, lines, cells, warehousing, and logistics (e.g., discrete event, physics, MILP)
- Synthetic data generation and validation approaches for model development
- Edge AI deployment (e.g., performance, reliability, lifecycle operations)
- Manufacturing and supply chain domain experience in areas such as discrete or process manufacturing, quality systems, maintenance and reliability, intralogistics, warehouse operations, plant OT/IT constraints, safety, and compliance.
- Ability to travel up to 50%, based on the work you do and the clients and industries/sectors you serve
- Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future.
Preferred
- Experience collaborating with technology partners (e.g., NVIDIA, Siemens, AWS. and/or similar ecosystems) to translate platforms into deliverable architectures and programs.
- Manufacturing and supply chain technology exposure in areas such as sensing and IIoT connectivity, process and product optimization, automation and process control, fleet operations, machine learning and data science, cybersecurity for OT
- Demonstrated experience leading client-facing advisory, PoCs, and implementations (not just research), including requirements, acceptance criteria, and operational handover.
- Graduate degree (MS/PhD) in Robotics, Computer Science, Electrical/Mechanical Engineering, Industrial Engineering, Applied Physics, Operations Research, or related field.
- Hands-on experience with NVIDIA ecosystem elements relevant to Physical AI (e.g., accelerated computing for vision/AI at the edge, simulation workflows, robotics stacks) and Siemens engineering platforms and tooling; ability to compare and compose with other vendor stacks.
- Experience designing governance and operating models for Physical AI in production: model monitoring and drift, incident response, data management, human-in-the-loop, safety and controls integration.
- Demonstrated thought leadership: reusable accelerators, reference architectures, demo assets, publications, or enablement delivered to internal/external audiences.
- Business development contribution (pipeline creation, proposal leadership, account expansion) and executive stakeholder management.
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $171,600 - $322,900.
You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.
Information for applicants with a need for accommodation:https://www2.deloitte.com/us/en/pages/careers/articles/join-deloitte-assistance-for-disabled-applicants.html
SCNOFY27
#EPCORE
Qualifications:Physical AI Senior Manager - Manufacturing & Supply Chain
We are a team of strategic advisors, architects, and implementers who drive business transformations. Our diverse talent energizes clients' business functions and technology to maximize value in Supply Chain enhancing their ability to fulfill their growth and efficiency ambitions. Imagine working with world-class supply network capabilities like Smart Factory, Strategy & Innovation, Supply Chain Responsiveness, Sourcing & Procurement, or Product Development & Operations!
Are you ready to take your career to new heights? Join our US Supply Chain & Network Operations Offering, where you'll deliver transformational solutions using operational expertise, digital technologies, advanced analytics, and industry-specific hybrid solutions. Don't miss the chance to be part of a team that provides exceptional client value while advancing your professional journey. Apply now and become a vital part of our innovative and dynamic workforce!
Recruiting for this role ends on 8/31/26.
The team
You will join a cross-functional Supply Chain & Manufacturing consulting environment focused on helping clients modernize operations through technology, data, and advanced engineering. The role operates in a matrix of industry practitioners, technologists, and alliance partners and requires strong collaboration, structured problem-solving, and the ability to translate emerging technology into operational results.
Work you'll do
You will serve as the functional and domain expert for Physical AI-where AI meets the physical world-across manufacturing and supply chain operations. You will shape advisory engagements, lead proofs of concept (PoCs), and drive implementation programs that combine robotics, computer vision, simulation, digital twins, synthetic data, and edge AI, frequently in partnership with ecosystem alliance providers (e.g., NVIDIA, Siemens, AWS and others). Candidates should be comfortable in factories, warehouses, and leadership conference rooms with the experience to translate between controls engineers, data scientists, and frontline operations.
Key responsibilities- Lead Physical AI strategy and advisory for manufacturing and supply chain clients. Identify high-value use cases (e.g., quality inspection, safety, intralogistics, material handling, asset monitoring, autonomous operations), define value hypotheses, and translate to roadmaps and business cases.
- Own solution shaping and end-to-end architecture spanning sensors, vision, data pipelines, model development, simulation, edge deployment, and operations (i.e., MLOps and ModelOps), with explicit acceptance criteria for operational environments.
- Drive PoCs and pilots to measurable outcomes. Define experiments, data collection plans, synthetic data approaches (when appropriate), evaluation metrics, and scale plans from pilot-to-plant and factory/network rollout.
- Integrate AI with real-world constraints: latency, reliability, safety, OT/IT connectivity, cybersecurity, model drift, human-in-the-loop workflows, and maintenance/operating model considerations.
- Partner with alliances and product teams to translate partner platforms into client-ready reference architectures, demos, and repeatable delivery assets.
- Influence pursuits and proposals: support scoping, estimating, staffing, risk and assumption framing, and executive-level storytelling. Serve as technical authority in client workshops and due diligence.
- Lead and mentor multi-disciplinary teams: data science, ML engineering, software and edge, vision, robotics and controls, manufacturing experts, and contribute to capability-building and market activation.
The team
Qualifications
Required
- Bachelor's degree or equivalent practical experience.
- 10+ years of relevant experience, including client leadership, team leadership, and sustained contribution to business development/pursuits.
- Experience in at least two of the following domains:
- Computer vision for industrial environments (e.g., inspection, defect detection, safety, tracking, manufacturing assembly)
- Robotics and autonomy (e.g., industrial robotics, mobile robotics and AMRs, perception-to-action workflows)
- Simulation, digital twins, physics-based modeling for factories, lines, cells, warehousing, and logistics (e.g., discrete event, physics, MILP)
- Synthetic data generation and validation approaches for model development
- Edge AI deployment (e.g., performance, reliability, lifecycle operations)
- Manufacturing and supply chain domain experience in areas such as discrete or process manufacturing, quality systems, maintenance and reliability, intralogistics, warehouse operations, plant OT/IT constraints, safety, and compliance.
- Ability to travel up to 50%, based on the work you do and the clients and industries/sectors you serve
- Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future.
Preferred
- Experience collaborating with technology partners (e.g., NVIDIA, Siemens, AWS. and/or similar ecosystems) to translate platforms into deliverable architectures and programs.
- Manufacturing and supply chain technology exposure in areas such as sensing and IIoT connectivity, process and product optimization, automation and process control, fleet operations, machine learning and data science, cybersecurity for OT
- Demonstrated experience leading client-facing advisory, PoCs, and implementations (not just research), including requirements, acceptance criteria, and operational handover.
- Graduate degree (MS/PhD) in Robotics, Computer Science, Electrical/Mechanical Engineering, Industrial Engineering, Applied Physics, Operations Research, or related field.
- Hands-on experience with NVIDIA ecosystem elements relevant to Physical AI (e.g., accelerated computing for vision/AI at the edge, simulation workflows, robotics stacks) and Siemens engineering platforms and tooling; ability to compare and compose with other vendor stacks.
- Experience designing governance and operating models for Physical AI in production: model monitoring and drift, incident response, data management, human-in-the-loop, safety and controls integration.
- Demonstrated thought leadership: reusable accelerators, reference architectures, demo assets, publications, or enablement delivered to internal/external audiences....