1

Manager Tensor Jobs in Spokane, WA (NOW HIRING)

Sr Software Engineer

Liberty Lake, WA · On-site

$123K - $162K/yr

... NUMA-aware memory management techniques to optimize memory access patterns for large-scale ... tensor computations and memory access patterns. • Knowledge of multi-threading, NUMA ...

Manager Tensor information

See Spokane, WA salary details

$33.9K

$107.8K

$183K

How much do manager tensor jobs pay per year?

As of Aug 22, 2026, the average yearly pay for manager tensor in Spokane, WA is $107,825.00, according to ZipRecruiter salary data. Most workers in this role earn between $75,800.00 and $134,000.00 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.

Sr Software Engineer

Positron AI

Liberty Lake, WA • On-site

$123K - $162K/yr

Full-time

Re-posted 16 days ago


Job description

Job Summary:
Positron AI specializes in developing custom hardware systems to accelerate AI inference, and they are seeking a Senior Software Engineer to contribute to the development of high-performance software for executing open-source large language models on their custom appliance. The role involves designing and implementing high-performance inference software, optimizing C++-based libraries, and collaborating with engineers to ensure efficient data movement between CPUs and FPGAs.
Responsibilities:
• Design and implement high-performance inference software for LLMs on custom hardware.
• Develop and optimize C++-based libraries that efficiently utilize SIMD instructions, threading, and memory hierarchy.
• Work closely with FPGA and systems engineers to ensure efficient data movement and computational offloading between x86 CPUs and FPGAs.
• Optimize model execution via low-level optimizations, including vectorization, cache efficiency, and hardware-aware scheduling.
• Contribute to performance profiling tools and methodologies to analyze execution bottlenecks at the instruction and data flow levels.
• Apply NUMA-aware memory management techniques to optimize memory access patterns for large-scale inference workloads.
• Implement ML system-level optimizations such as token streaming, KV cache optimizations, and efficient batching for transformer execution.
• Collaborate with ML researchers and software engineers to integrate model quantization techniques, sparsity optimizations, and mixed-precision execution.
• Ensure all code contributions include unit, performance, acceptance, and regression tests as part of a continuous integration-based development process.
Qualifications:
Required:
• 7+ years of professional experience in C++ software development, with a focus on performance-critical applications.
• Strong understanding of C++ templates and modern memory management.
• Hands-on experience with SIMD programming (AVX-512, SSE, or equivalent) and intrinsics-based vectorization.
• Experience in high-performance computing (HPC), numerical computing, or ML inference optimization.
• Experience with ML model execution optimizations, including efficient tensor computations and memory access patterns.
• Knowledge of multi-threading, NUMA architectures, and low-level CPU optimization.
• Proficiency with systems-level software development, profiling tools (perfetto, VTune, Valgrind), and benchmarking.
• Experience working with hardware accelerators (FPGAs, GPUs, or custom ASICs) and designing efficient software-hardware interfaces.
Preferred:
• Familiarity with LLVM/Clang or GCC compiler optimizations.
• Experience in LLM quantization, sparsity optimizations, and mixed-precision computation.
• Knowledge of distributed inference techniques and networking optimizations.
• Understanding of graph partitioning and execution scheduling for large-scale ML models.
Company:
Positron delivers vendor freedom and faster inference for both enterprises and research teams, by allowing them to use hardware and software explicitly designed from the ground up for generative and large language models (LLMs). Founded in 2023, the company is headquartered in Reno, USA, with a team of 11-50 employees. The company is currently Early Stage.