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Tensor Jobs (NOW HIRING)

Apolis

On-prem Platform Engineer

Charlotte, NC · Hybrid

Tensor parallelism and large model scaling * CUDA, NCCL, GPU architecture * GPU partitioning & optimization (MIG) * Kubernetes & ML Serving * Kubernetes-based ML serving platforms * KServe, OpenShift ...

TekGlobal

On-prem Platform Engineer

Charlotte, NC · Hybrid

Tensor parallelism and large model scaling * CUDA, NCCL, GPU architecture * GPU partitioning & optimization (MIG) Kubernetes & ML Serving * Kubernetes-based ML serving platforms * KServe, OpenShift ...

New

Diverse Lynx

On-prem Platform Engineer

Charlotte, NC · Hybrid

$80/hr

Tensor parallelism and large model scaling * CUDA, NCCL, GPU architecture * GPU partitioning & optimization (MIG) Kubernetes & ML Serving * Kubernetes-based ML serving platforms * KServe, OpenShift ...

Lorven Technologies

AI Architect - Poway, CA

Poway, CA · On-site

Python (PyTorch/Tensor Flow) deployed on Red Hat Enterprise Linux (RHEL) tactical edge servers. * Specific GA Tech: Familiarity with DDS (Data Distribution Service) middleware (RTI Connext) used in ...

Baseten

Post-Training Research Engineer

San Francisco, CA · On-site

$200K - $275K/yr

Advanced experience in a tensor/array computation library like PyTorch, TensorFlow, Jax, or similar * A detailed understanding of transformer training parallelism strategies like data parallelism ...

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How much do tensor jobs pay per year?

As of Jun 4, 2026, the average yearly pay for tensor in the United States is $165,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,500.00 and $170,000.00 per year, depending on experience, location, and employer.

What is a Tensor job?

A Tensor job typically refers to a role involving tensors, which are mathematical objects used in machine learning, AI, and scientific computing. These jobs often require expertise in deep learning frameworks like TensorFlow or PyTorch, where tensors represent multi-dimensional arrays for data processing. A Tensor job can involve designing and optimizing neural networks, performing large-scale data analysis, or working with high-performance computing.

What are the key skills and qualifications needed to thrive as a Tensor?

I'm sorry, but 'Tensor' is not recognized as a real-world professional occupation.

What are some common challenges faced by TensorFlow Developers when working on large-scale machine learning projects?

TensorFlow Developers often encounter challenges such as optimizing model performance for large datasets, managing distributed training across multiple GPUs or TPUs, and ensuring reproducibility of experiments. Collaboration with data engineers and DevOps teams is essential to streamline data pipelines and deployment workflows. Staying up to date with frequent updates in the TensorFlow ecosystem and best practices for model optimization is also crucial for success in this role.

What are Tensor jobs?

Tensor jobs typically refer to roles involving the use or development of tensors, which are mathematical objects used in machine learning and deep learning. These jobs often include positions like machine learning engineer, data scientist, or deep learning researcher, where working with tensor-based libraries such as TensorFlow or PyTorch is common. Responsibilities may include designing and training neural networks, processing multidimensional data, and optimizing machine learning models for performance. Candidates for these roles usually need a strong background in mathematics, programming, and data analysis.

What is the difference between Tensor vs Data Scientist?

AspectTensorData Scientist
Required CredentialsKnowledge of machine learning, programming skills, often a degree in computer science or related fieldsDegree in statistics, computer science, or related fields; strong analytical skills
Work EnvironmentTech companies, AI research labs, software development teamsBusiness, finance, healthcare, and tech industries analyzing data to inform decisions
Industry UsagePrimarily in AI, machine learning, and deep learning projectsAcross industries for data analysis, predictive modeling, and insights

While a Tensor is a fundamental data structure used in machine learning frameworks like TensorFlow, a Data Scientist analyzes data to extract insights and build models. Tensors are tools that Data Scientists often work with, but they are not roles themselves. Understanding tensors is essential for Data Scientists involved in AI and machine learning projects.

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What cities are hiring for Tensor jobs? Cities with the most Tensor job openings:
What are the most commonly searched types of Tensor jobs? The most popular types of Tensor jobs are:
What states have the most Tensor jobs? States with the most job openings for Tensor jobs include:
What job categories do people searching Tensor jobs look for? The top searched job categories for Tensor jobs are:
Tensor jobs near you
Infographic showing various Tensor job openings in the United States as of May 2026, with employment types broken down into 1% Locum Tenens, 97% Full Time, 1% Temporary, and 1% Contract. Highlights an 89% Physical, 5% Hybrid, and 6% Remote job distribution, with an average salary of $165,018 per year, or $79.3 per hour.
On-prem Platform Engineer

On-prem Platform Engineer

Apolis

Charlotte, NC • Hybrid

Other

This job post has expired today. Applications are no longer accepted.

Job description

On-prem Platform Engineer

Location: Charlotte, NC

Key Skills

Must-Have Skills (Mandatory Keywords)

  • LLM Inference & Optimization
    • vLLM, TensorRT-LLM, Triton Inference Server, SGLang
    • Inference optimization techniques:
      • Continuous batching
      • Speculative decoding
      • KV cache / Prefix caching
    • Model optimization:
      • FP8, AWQ, GPTQ
  • Distributed & GPU Systems
    • Tensor parallelism and large model scaling
    • CUDA, NCCL, GPU architecture
    • GPU partitioning & optimization (MIG)
  • Kubernetes & ML Serving
    • Kubernetes-based ML serving platforms
    • KServe, OpenShift AI
    • Helm charts, Operators, platform automation
  • GPU Orchestration
    • Run:AI or similar GPU scheduling/orchestration platforms
    • Multi-tenant GPU workload management
  • Platform Engineering
    • Experience building internal AI/ML platforms (on-prem or hybrid)
    • Strong automation and system design mindset
  • Observability & Performance
    • Prometheus, Grafana
    • ML observability (model latency, throughput, drift, resource utilization)
    • Performance benchmarking and tuning

Good to Have / Preferred Skills

  • Experience with LLMOps / GenAI pipelines
  • Exposure to hybrid cloud (on-prem + GCP/Azure integration)
  • Familiarity with Inferentia / alternative accelerators
  • Knowledge of service mesh / networking in GPU clusters

Build, configure, and operate on‐prem Kubernetes/OpenShift AI platforms for deploying and serving GenAI models and LLM inference workloads.

Design and optimize high‐performance inference stacks using vLLM, TensorRT‐LLM, Triton Inference Server, SGLang, and advanced techniques (continuous batching, speculative decoding, KV caching).

Manage GPU orchestration and capacity using Run:AI, MIG, CUDA/NCCL, and tensor parallelism to maximize utilization and throughput.

Deploy and operate Kubernetes ML serving frameworks (KServe, Helm, Operators) for scalable, reliable model serving.

Drive inference optimization and benchmarking, leveraging FP8, AWQ, GPTQ, and performance tools such as GuideLLM and Locust.

Implement observability and ML monitoring using Prometheus, Grafana, Arize AI, ensuring SLA/SLO compliance for GenAI services.

Collaborate with ML and research teams to onboard new models, tune inference performance, and productionize GenAI use cases.

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