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Manager Tensor Jobs in Texas (NOW HIRING)

iFlow Inc

Program Manager

Austin, TX · On-site

Job Title: Program Manager Location : Austin,TX Duration: 6 Months Experience: 5-15 Years ... Integrate SGLang with the Run:ai Model Streamer to enable concurrent tensor streaming directly to ...

Graphcore

AI Platform Architect

Austin, TX · On-site

... or Slurm to manage distributed training jobs, model check-pointing, and inference serving at ... tensor/pipeline parallelism and the infrastructure requirements of modern LLM architectures. • ...

Elevance Health

Gen AI Engineer

Grand Prairie, TX · Hybrid

Excellent written & verbal communication and stakeholder management skills. * 4+ years project ... GPU memory optimization techniques (tensor parallelism, pipeline parallelism); LLM caching ...

Graphcore

AI Platform Architect

Austin, TX · On-site

... manage distributed training jobs, model check-pointing, and inference serving at massive scale ... Deep practical knowledge of how large models are trained and deployed, including data/tensor ...

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Manager Tensor information

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.

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 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 cities in Texas are hiring for Manager Tensor jobs? Cities in Texas with the most Manager Tensor job openings:
Manager Tensor jobs near you

Program Manager

iFlow Inc

Austin, TX • On-site

Contractor

Re-posted 26 days ago

Job description

Job Title: Program Manager
Location: Austin,TX
Duration: 6 Months
Experience: 5-15  Years

Description:
We are mainly looking for a ML Engineer who is experienced and ready to take on this role. The candidate should have a strong background in ML and be capable of handling the tasks and responsibilities that come with the position.
ML Infrastructure & Performance Engineer
Focus: This role focuses on the "serving plane." The engineer will integrate high-speed inference runtimes with streaming loaders and take ownership of the performance benchmarking mandate.
Key Responsibilities:
Integrate SGLang with the Run:ai Model Streamer to enable concurrent tensor streaming directly to GPU memory, reducing model "cold start" times.
Optimize SGLang’s backend runtime, leveraging features like RadixAttention for prefix caching and compressed finite-state machines for faster decoding.
Design and execute rigorous performance benchmarking suites to identify bottlenecks in the inference stack and provide code-level "fixes" to improve time-to-first-token (TTFT).
Required Expertise:
Proficiency in Python and experience with asynchronous programming (AsyncIO) for ML serving frameworks.
Experience with Ray for distributed compute and managing Reinforcement Learning (RL) workloads.
Hands-on experience with profiling tools such as NVIDIA Nsight, PyTorch Profiler, or Intel Gaudi instrumentation.