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Manager Tensor Jobs in Raleigh, NC (NOW HIRING)

Manager Tensor information

See Raleigh, NC salary details

$32.6K

$103.7K

$175.9K

How much do manager tensor jobs pay per year?

As of Aug 22, 2026, the average yearly pay for manager tensor in Raleigh, NC is $103,661.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,900.00 and $128,800.00 per year, depending on experience, location, and employer.

What is a Manager Tensor?

A Manager Tensor is typically a managerial position responsible for overseeing teams that develop and implement machine learning models using TensorFlow or similar tensor-based frameworks. This role involves coordinating data science and engineering teams, ensuring project goals align with business objectives, and facilitating the deployment of scalable AI solutions. Additionally, a Manager Tensor may be tasked with mentoring staff, managing resources, and staying updated with the latest advancements in artificial intelligence. The position requires strong leadership, technical expertise in machine learning, and experience with deep learning platforms.

What are the key skills and qualifications needed to thrive as a Manager Tensor, and why are they important?

To thrive as a Manager Tensor (commonly referred to as a TensorFlow Manager or Machine Learning Manager), you need a solid background in machine learning, deep learning frameworks (especially TensorFlow), and experience leading technical teams, typically backed by a relevant degree. Proficiency with TensorFlow, Python, data engineering tools, and cloud platforms, along with certifications in machine learning, are highly valued. Leadership, strong communication, and project management skills help you effectively guide teams and collaborate with stakeholders. These skills ensure successful project delivery, innovation, and alignment with organizational goals in complex AI-driven environments.

What are some common challenges faced by a Manager Tensor when leading AI and machine learning teams?

A Manager Tensor often encounters challenges such as balancing technical leadership with strategic oversight, managing projects that involve complex and evolving technologies, and ensuring effective communication among data scientists, engineers, and stakeholders. Additionally, staying current with rapid advancements in AI frameworks and guiding the team through best practices can be demanding. Collaboration across multidisciplinary teams and aligning projects with business objectives are also key aspects of the role.

What is the difference between Manager Tensor vs Data Scientist?

AspectManager TensorData Scientist
Required CredentialsBachelor's or Master's in Computer Science, Data Analytics, or related fields; certifications like TensorFlow Developer are commonBachelor's or Master's in Data Science, Statistics, Computer Science; certifications like Certified Data Scientist are common
Work EnvironmentLeads teams, manages projects, collaborates with stakeholders in tech or AI-focused companiesAnalyzes data, builds models, reports insights in tech, finance, healthcare industries
Employer & Industry UsageUsed in AI, machine learning, and tech companies for managing TensorFlow projectsUsed across industries for data analysis, predictive modeling, and research

The main difference is that a Manager Tensor oversees AI projects involving TensorFlow, focusing on team management and project delivery, while a Data Scientist primarily analyzes data and builds models. Both roles require technical knowledge, but the Manager Tensor role emphasizes leadership and project management within AI initiatives.

Senior Software Engineer - GPU Local AI Platforms

Nvidia

Durham, NC

$118K - $156K/yr

Full-time

Posted 29 days ago


Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

7th of 246 rated software companies


Job description

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology-and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent. As an NVIDIAN, you'll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.

NVIDIA's Local AI team is building the software stack that makes large language models and generative AI applications run at maximum efficiency on NVIDIA edge AI hardware. The AI ecosystem moves fast; our job is to make sure end users get the best experience. We own the platform - performance, CI/CD pipelines, validated recipes, and model bring-up infrastructure - that lets developers run groundbreaking LLMs out of the box. The open-source community builds fast; our platform is what turns community innovation into something developers and partners can rely on at scale.

What you'll be doing:

  • Track and evaluate innovations in leading open-source LLM inference frameworks - identify performance-critical features and algorithmic improvements relevant to NVIDIA edge AI hardware

  • Analyze how new model architectures and inference algorithms (attention variants, MoE routing, speculative decoding, multi-token prediction, quantized inference) map onto NVIDIA GPU architecture - identify mismatch, fallback paths, and optimization opportunities

  • Characterize multi-node inference behavior: collective communication primitives (NCCL/RCCL), topology-aware all-reduce strategies, and parallelism efficiency on edge cluster configurations

  • Produce performance analysis reports mapping theoretical hardware limits (memory bandwidth, FLOP/s, interconnect throughput) to observed inference throughput, latency, and utilization

  • Own the model validation workflow for new model releases: architecture compatibility assessment, inference recipe development, performance characterization, and publication to developer recipe sites

  • Develop and maintain developer-facing inference recipes: keep them accurate as frameworks evolve, automate staleness detection, and build feedback loops from CI results to recipe updates

  • Engage with community and partners on model bring-up questions; serve as the technical point of contact for hardware-specific inference issues related to partner concerns

What we need to see:

  • BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience.

  • 12+ years of software engineering with depth in GPU computing, ML systems, or high-performance inference

  • Strong Python or C++ programming, software design, and software engineering skills.

  • Hands-on experience with GPU kernel development or optimization (CUDA/C++, Triton, or equivalent) - you understand how thread blocks, memory hierarchy, and warp execution affect real-world performance

  • Working knowledge of LLM inference internals: attention mechanisms, KV-cache management, continuous batching, quantization formats, and tensor parallelism

  • Container engineering expertise: multi-architecture Docker or OCI builds, layer optimization, runtime configuration, NVIDIA Container Toolkit

  • Strong analytical skills: ability to form a performance hypothesis, design an experiment, interpret results, and communicate findings clearly

Widely considered to be one of the technology world's most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 5, and 272,000 USD - 431,250 USD for Level 6.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until July 28, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993