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Neuroscience - AI Training - Columbus, US

... AI-related training or evaluation tasks • Background in scientific publishing or peer review Company : Building the most advanced global infrastructure for People Science. Founded in 2014, the ...

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Neuroscience - AI Training - Memphis, US

... AI-related training or evaluation tasks • A background in scientific publishing or peer review Company : Building the most advanced global infrastructure for People Science. Founded in 2014, the ...

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Neuroscience - AI Training - Denver, US

... AI-related training or evaluation tasks • A background in scientific publishing or peer review Company : Building the most advanced global infrastructure for People Science. Founded in 2014, the ...

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Neuroscience - AI Training - Durham, US

... AI-related training or evaluation tasks • Background in scientific publishing or peer review Company : Building the most advanced global infrastructure for People Science. Founded in 2014, the ...

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Neuroscience - AI Training - Portland, US

... AI-related training or evaluation tasks • Background in scientific publishing or peer review Company : Building the most advanced global infrastructure for People Science. Founded in 2014, the ...

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How much do ai training jobs pay per year?

As of Jun 10, 2026, the average yearly pay for ai training in the United States is $68,682.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,000.00 and $84,500.00 per year, depending on experience, location, and employer.

What is an AI Training job?

An AI Training job involves preparing and refining data to help improve artificial intelligence models. This can include labeling data, reviewing AI-generated outputs, and providing feedback to enhance accuracy. AI Trainers work with machine learning teams to fine-tune models for tasks like chatbots, image recognition, or language processing. Strong attention to detail and an understanding of AI behavior are key skills for this role.

What are the typical responsibilities of an AI Training specialist on a day-to-day basis?

AI Training specialists are primarily responsible for curating, labeling, and managing large datasets that are used to train machine learning models. They often collaborate closely with data scientists and engineers to ensure data quality, clarity of labeling guidelines, and that project requirements are met. Daily tasks can include annotating images or text, performing data validation, refining annotation protocols, and reviewing outputs generated by AI models. This role frequently involves teamwork as well as independent problem-solving, offering opportunities to learn about AI development workflows and expand technical knowledge.

What are the key skills and qualifications needed to thrive in the Ai Training position, and why are they important?

To thrive in AI Training, you need a solid understanding of machine learning concepts, data annotation, and statistical analysis, often supported by a background in computer science or related fields. Familiarity with tools such as TensorFlow, PyTorch, labeling software, and experience with data management systems is highly valued. Strong attention to detail, effective communication, and critical thinking are important soft skills for this role. These skills ensure the accurate preparation and handling of training data, which directly impacts the quality and success of AI models.

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What cities are hiring for Ai Training jobs? Cities with the most Ai Training job openings:
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Ai Training jobs near you
Infographic showing various Ai Training job openings in the United States as of June 2026, with employment types broken down into 70% Full Time, 14% Part Time, and 16% Contract. Highlights an 86% In-person, and 14% Remote job distribution, with an average salary of $68,682 per year, or $33 per hour.

Senior Software Engineer, AI Training & Infrastructure

Skild AI

San Mateo, CA • On-site, Remote

$139K - $183K/yr

Other

Posted 2 days ago

Job description

Position Overview

Skild AI, Inc. seeks a Senior Software Engineer, AI Training & Infrastructure in San Mateo, CA responsible for building and scaling training infrastructure and tools that support the full ML lifecycle-data preparation, training orchestration, evaluation, and deployment-for real-world robotics applications. This includes performance, reliability, observability, and developer productivity across distributed training systems, as well as data processing for multimodal datasets, performance tuning of training jobs, and media processing/compression (e.g., ffmpeg). Specific duties include: (i) architecting, building, and maintaining distributed training pipelines and frameworks spanning data ingest/preprocessing, large-scale training, and evaluation; (ii) optimizing training performance and resource utilization by identifying bottlenecks and implementing improvements in data loading, I/O, caching, sharding, and prefetching; (iii) integrating state-of-the-art ML techniques into production training systems in collaboration with research/ML teams; (iv) implementing monitoring, logging, alerting, automated testing, and CI/CD for reliable training operations; and (v) developing developer tooling and documentation, including dashboards and utilities, to streamline experimentation at scale and improve engineer productivity.

Responsibilities
  • Architecting, building, and maintaining distributed training pipelines and frameworks spanning data ingest/preprocessing, large-scale training, and evaluation.
  • Optimizing training performance and resource utilization by identifying bottlenecks and implementing improvements in data loading, I/O, caching, sharding, and prefetching.
  • Integrating state-of-the-art ML techniques into production training systems in collaboration with research/ML teams.
  • Implementing monitoring, logging, alerting, automated testing, and CI/CD for reliable training operations.
  • Developing developer tooling and documentation, including dashboards and utilities, to streamline experimentation at scale and improve engineer productivity.
Minimum Requirements
  • Must have a master's degree (or foreign equivalent) in Computer Science, Robotics, Engineering, or a related field and two (2) years of experience in machine learning infrastructure. Experience can be concurrent.
  • Must also have two (2) years of experience designing and operating distributed training pipelines at scale, including data preprocessing, orchestration, and evaluation. Experience can be concurrent.
  • Must have any experience with each of the following: (i) Python or C++ and at least one deep learning library (e.g., PyTorch, TensorFlow, or JAX); and (ii) CI/CD and automated testing for ML/infra services. Experience can be concurrent.
  • Must have knowledge of: (i) optimizing data loading and I/O for deep learning workloads (e.g., PyTorch DataLoader, sharding, prefetching, or caching); (ii) processing multimodal datasets and formats (e.g., HDF5, TFRecord, Parquet, or equivalent) and image processing/compression (e.g., OpenCV or ffmpeg); (iii) cloud-based training in AWS, Google Cloud, or Azure; (iv) implementing monitoring, logging, and alerting for training systems; (v) Linux OS fundamentals and operation at large scale; (vi) distributed systems and ML training techniques/models; and (vii) core software engineering principles, including algorithms, data structures, and system design. Experience can be concurrent.

Apply online at skild.ai/career.

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