Join Skild AI as a Software Engineer to develop and optimize software infrastructure for training advanced AI models in robotics.
Posted by employer 2 years ago
First seen on Joblaze 2 months ago
Last verified on the company career page 1 day ago
Skills & Technologies
AI in the day-to-day
You will explore new ways to efficiently make use of many types of data in our training pipeline.
Requirements
- Experience
- 3+ years
- Education
- Bachelor's degree
Not disclosed in this posting: work arrangement, visa sponsorship.
Joblaze summary
In this role, the Software Engineer focuses on developing and optimizing the software infrastructure for training advanced AI models, ensuring efficient and scalable training pipelines. Key skills include proficiency in Python or C++, experience with deep learning libraries like PyTorch or TensorFlow, and a solid understanding of distributed computing. This position is ideal for candidates with at least three years of industry experience, particularly those who thrive in innovative environments and are eager to tackle complex challenges in AI and robotics.
Joblaze insights
- Listed about 2 months ago — first seen on Joblaze September 3, 2026. Last confirmed on Skild AI's careers page November 2, 2026.
- Salary band is below the typical range for AI/ML roles (median ~$190,000).
- Starts above 8% of 154 comparable mid ai/ml roles that list Python we track (median $150,000 across 61 companies). See Python salary trends
- Python appears in 48.2% of 830 comparable mid ai/ml roles; distributed computing appears in 0.1% of 830 comparable mid ai/ml roles.
Quick facts
- What's the salary range?
- Skild AI lists $100,000–$300,000 for this role.
- How much experience is required?
- At least 3 years of relevant experience for this Software Engineer, AI Training and Infrastructure role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: AWS, Azure, C++, Google Cloud, JAX, PyTorch.
- What seniority level is this role?
- Skild AI targets mid-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Software Engineer, AI Training and Infrastructure role at Skild AI.
From the original posting
Company Overview
Position Overview
We are looking for a Software Engineer to work at the forefront of developing and optimizing the software infrastructure and tools necessary for training cutting-edge AI models. You will focus on building robust, scalable, and efficient training pipelines and frameworks that support the entire machine learning lifecycle, from data preparation to model deployment. You will collaborate with researchers and machine learning engineers to ensure seamless integration and operation of training systems, pushing the boundaries of what AI can achieve in real-world robotics applications. You will explore new ways to efficiently make use of many types of data in our training pipeline.
Responsibilities
- Develop and maintain robust, scalable, and distributed training pipelines (data preprocessing, training orchestration, and model evaluation) and frameworks for large-scale AI models.
- Optimize training processes for performance and resource utilization, ensuring scalability and reliability.
- Collaborate with researchers and machine learning engineers to integrate state-of-the-art algorithms and techniques into training pipelines.
- Monitor and analyze training, identifying bottlenecks and proposing solutions to improve efficiency and performance.
- Ensure the robustness and reliability of the training infrastructure, including automated testing and continuous integration.
Preferred Qualifications
- BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.
- Minimum of 3 years of industry experience.
- Proficiency in Python, C++, or similar and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc.
- Strong background in distributed computing, parallel processing techniques, handling large-scale datasets and data preprocessing.
- Deep understanding of state-of-the-art machine learning techniques and models.
- Experience with cloud-based training environments (AWS, Google Cloud, Azure).
- Experience in developing and maintaining software tooling and infrastructure for machine learning.
- Deep understanding and practical experience with software engineering principles, including algorithms, data structures, and system design.
- Experience with continuous integration and automated testing frameworks.
Standard company text repeated across Skild AI's postings is omitted here.