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Manager Tensor Jobs (NOW HIRING)

Accellor

AI Principal Engineer

San Francisco, CA · On-site

... management, conversation memory, tool context, agent state, multimodal context, source grounding ... Experience with tensor parallelism, pipeline parallelism, model sharding, KV-cache optimization ...

Accellor

AI Principal Engineer

Mountain View, CA · On-site

... management, conversation memory, tool context, agent state, multimodal context, source grounding ... Experience with tensor parallelism, pipeline parallelism, model sharding, KV-cache optimization ...

Accellor

AI Principal Engineer

San Francisco, CA · On-site

... management, conversation memory, tool context, agent state, multimodal context, source grounding ... Experience with tensor parallelism, pipeline parallelism, model sharding, KV-cache optimization ...

RooflineAI GmbH

$88K - $121K/yr

... management * Lead technical development and roadmaps for your part of the stack, working with ... Hands-on experience with MLIR or similar compiler frameworks for tensor/graph workloads * Proven ...

Accellor

AI Principal Engineer

San Francisco, CA

... management, conversation memory, tool context, agent state, multimodal context, source grounding ... Experience with tensor parallelism, pipeline parallelism, model sharding, KV-cache optimization ...

Accellor

AI Principal Engineer

San Francisco, CA · On-site

$180 - $240/hr

... management, conversation memory, tool context, agent state, multimodal context, source grounding ... Experience with tensor parallelism, pipeline parallelism, model sharding, KV-cache optimization ...

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How much do manager tensor jobs pay per year?

As of Aug 22, 2026, the average yearly pay for manager tensor in the United States is $106,639.00, according to ZipRecruiter salary data. Most workers in this role earn between $75,000.00 and $132,500.00 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.

What cities are hiring for Manager Tensor jobs?

Cities with the most Manager 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 Manager Tensor jobs?

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Manager Tensor jobs near you

Machine Learning Engineer - Distributed ML Systems

Pluralis Research

San Francisco, CA • On-site

Other

Re-posted 23 days ago

Job description

Overview

Pluralis Research carries out foundational research on Protocol Learning: multi-participant training of foundation models where no single participant has, or can ever obtain, a full copy of the model. The purpose of Protocol Learning is to facilitate the creation of community-trained and community-owned frontier models with self-sustaining economics.

We’re looking for Senior/Staff engineers with 5+ years of experience in distributed systems and ML large‑scale training. You’ll be implementing a novel substrate for training distributed ML models that work under consumer grade internet connection.

Responsibilities Distributed Training Architecture & Optimization

  • Design and implement large‑scale distributed training systems optimized for heterogeneous hardware operating under low‑bandwidth, high‑latency conditions.

  • Develop and optimize model‑parallel training strategies (data, tensor, pipeline parallelism) with custom sharding techniques that minimize communication overhead.

  • Optimize GPU utilization, memory efficiency, and compute performance across distributed nodes.

  • Implement robust checkpointing, state synchronization, and recovery mechanisms for long‑running, fault‑prone training jobs.

  • Build monitoring and metrics systems to track training progress, model quality, and system bottlenecks.

Decentralized Networking & Resilience

  • Architect resilient training systems where nodes can fail, networks can partition, and participants can dynamically join or leave.

  • Design and optimize peer‑to‑peer topologies for decentralized coordination across non‑co‑located nodes.

  • Implement NAT traversal, peer discovery, dynamic routing, and connection lifecycle management.

  • Profile and optimize communication patterns to reduce latency and bandwidth overhead in multi‑participant environments.

What You’ll Bring

  • Strong experience building and operating distributed systems in production.

  • Hands‑on expertise with distributed training frameworks (FSDP, DeepSpeed, Megatron, or similar).

  • Deep understanding of model parallelism (data, tensor, pipeline parallelism).

  • Expert‑level Python with production experience (concurrency, error handling, retry logic, clean architecture).

  • Strong networking fundamentals: P2P systems, gRPC, routing, NAT traversal, distributed coordination.

  • Experience optimizing GPU workloads, memory management, and large‑scale compute efficiency.

What We Offer

  • Equity‑heavy compensation with meaningful ownership in a mission‑driven company

  • Competitive base salary for senior engineering roles in Australia

  • Visa sponsorship available for exceptional candidates

  • Remote‑first with optional access to our Melbourne hub

  • World‑class team — team mates were previously at Google, Amazon, Microsoft, and leading startups

Backed by Union Square Ventures and other tier‑1 investors, we’re a world‑class, deeply technical team of ML researchers and engineers. Pluralis is unapologetically ideological. We view the world as a better place if we are able to implement what we are attempting, and Protocol Learning as the only plausible approach to preventing a handful of massive corporations monopolising model development, access and release, and achieving massive economic capture. If this resonates, please apply.

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