Seoul, South Korea – FuriosaAI, a leading innovator in Artificial Intelligence processing technology, is actively recruiting talented AI Software Engineers to join its Platform Team. Teh company, with locations in Seoul and offering global remote opportunities, aims to enhance its Software Development Kit (SDK) with models optimized for its proprietary Tensor Contraction Processor (TCP) accelerator.
Advancing AI Model Optimization
Table of Contents
- 1. Advancing AI Model Optimization
- 2. key Responsibilities and Technical Focus
- 3. Required Skills and Experience
- 4. the Growing Demand for AI Specialization
- 5. Frequently Asked Questions
- 6. Describe a scenario where you would choose Terraform over CloudFormation for infrastructure as code, and why.
- 7. AI Software Engineer (Platform Software) – A Focused Role Description: Content Writer for Indiswork
- 8. Understanding teh Evolving Landscape of AI Platform Engineering
- 9. Core Responsibilities: Building the AI Engine Room
- 10. Essential Skills & Qualifications: The tech Stack
- 11. The Indiswork Advantage: What Sets Our Platform Engineering Team Apart
The Platform Team is responsible for building the core software infrastructure that empowers AI developers to deploy high-performance models on FuriosaAI NPUs. This encompasses developing the runtime surroundings, a robust Large Language Model (LLM) serving framework, and crucial pytorch models and extensions. According to recent industry reports, the demand for optimized AI infrastructure is surging, driven by the exponential growth of generative AI applications.
key Responsibilities and Technical Focus
Triumphant candidates will be deeply involved in several critical areas. Primarily, the role requires developing and optimizing Deep Neural Network (DNN) model implementations within the PyTorch framework, specifically tailored for the FuriosaAI TCP architecture. Furthermore, Engineers will analyze existing AI model inference frameworks-including vLLM, tensorrt-LLM, and DeepSpeed-MII-examining their features, implementations, CUDA kernels, and Triton technologies.
Research and implementation of generative AI models, advanced parallelism strategies, and cutting-edge inference techniques will also be central to the position. collaboration with the compiler team is paramount, ensuring seamless optimization and enablement of these models. This collaborative approach is crucial for unlocking the full potential of FuriosaAI’s hardware.
Required Skills and Experience
Applicants should possess a Bachelor of Science degree in Computer Science, Engineering, or a related field, or demonstrate equivalent industry experience. A strong foundation in Python programming is essential, alongside practical experience developing AI models using DNN frameworks like PyTorch. A extensive understanding of Machine Learning, Deep Learning, Natural Language Processing (NLP), and Generative AI models is also required.
Moreover, the ideal candidate will demonstrate exceptional interaction skills and a proven ability to collaborate effectively within cross-functional teams. A track record of contributing to open-source projects is highly valued. Desired experience includes working with PyTorch 2.0 technologies like TorchDynamo, as well as DNN compiler technologies such as Triton and MLIR. Proficiency in C++,CUDA,or Rust is also beneficial.
| Skill/Experience | Required | Preferred |
|---|---|---|
| Python Programming | Yes | |
| PyTorch Experience | Yes | PyTorch 2.0 (TorchDynamo) |
| DNN Frameworks | Yes | |
| ML/DL/NLP/generative AI | Yes | |
| C++/CUDA/Rust | yes | |
| LLM Frameworks | vLLM, TensorRT-LLM, DeepSpeed-MII |
Did You Know? The global AI chip market is projected to reach $300 billion by 2030, signaling ample growth and prospect for companies like FuriosaAI.
Pro Tip: Familiarity with model quantization and evaluation techniques will substantially strengthen your application, as these are critical for optimizing performance and efficiency.
Are you prepared to contribute to the evolution of AI hardware and software? What role do you see AI accelerators playing in the future of computing?
the Growing Demand for AI Specialization
The rise of Artificial Intelligence is creating an unprecedented demand for specialized roles, especially in areas like AI software engineering. Companies are increasingly seeking engineers who not only understand the theoretical foundations of AI but also possess the practical skills to optimize models for specific hardware architectures. This trend is expected to continue, as AI becomes more deeply integrated into various industries.
Frequently Asked Questions
- What is an NPU? An NPU, or Neural Processing Unit, is a specialized hardware accelerator designed to efficiently perform the computations required for AI tasks.
- what is PyTorch? PyTorch is a popular open-source machine learning framework used for developing and training AI models.
- What are LLMs? LLMs, or Large Language Models, are a type of AI model capable of understanding and generating human-like text.
- What is the meaning of model optimization? Optimizing AI models reduces computational costs and improves performance,making them more suitable for real-world applications.
- What skills are most valuable for an AI Software Engineer? Proficiency in Python, experience with DNN frameworks like PyTorch, and a strong understanding of machine learning concepts are highly valued.
Interested candidates are encouraged to apply through the company’s careers page. This is an opportunity to join a dynamic team shaping the future of AI computing.
Share this article with your network and leave a comment below with your thoughts on the future of AI acceleration!
Describe a scenario where you would choose Terraform over CloudFormation for infrastructure as code, and why.
AI Software Engineer (Platform Software) – A Focused Role Description: Content Writer for Indiswork
Understanding teh Evolving Landscape of AI Platform Engineering
The demand for skilled AI Software Engineers is surging, notably those specializing in platform software. This isn’t just about building AI models; it’s about creating the robust, scalable infrastructure that supports those models. Indiswork, a leading innovator in AI-driven solutions, requires a specialized Content Writer to articulate the nuances of this critical role. This article details the responsibilities, required skills, and career trajectory for an AI Platform Software Engineer, geared towards attracting top talent. We’ll focus on the unique demands of building and maintaining the underlying systems for artificial intelligence and machine learning (ML) applications.
Core Responsibilities: Building the AI Engine Room
An AI Software Engineer (Platform) at Indiswork isn’t directly crafting algorithms (though understanding them is crucial). Instead, they are the architects and builders of the systems that allow data scientists and ML engineers to deploy and scale those algorithms effectively. Key responsibilities include:
* Developing and Maintaining AI Infrastructure: this encompasses everything from data pipelines and storage solutions to model serving frameworks and monitoring tools. Think Kubernetes, Docker, cloud platforms (AWS, Azure, GCP), and data lakes.
* Optimizing Performance & Scalability: AI models are resource-intensive. Engineers must optimize code, infrastructure, and algorithms for speed, efficiency, and the ability to handle massive datasets and user loads. This frequently enough involves distributed systems and parallel processing.
* Implementing MLOps Practices: MLOps (Machine Learning Operations) is the bridge between growth and production. This role is heavily involved in automating the ML lifecycle – from model training and validation to deployment and monitoring. tools like MLflow,Kubeflow,and TensorFlow Extended (TFX) are essential.
* Ensuring Data Security & Compliance: Handling sensitive data requires a strong understanding of data privacy regulations (e.g., GDPR, CCPA) and security best practices. Data governance and access control are paramount.
* Collaboration with Data Scientists & ML Engineers: This role is highly collaborative.Engineers work closely with data scientists to understand their needs and translate them into scalable, reliable infrastructure.
Essential Skills & Qualifications: The tech Stack
The ideal candidate for an AI Platform Software Engineer position at Indiswork possesses a strong foundation in computer science and a passion for building scalable systems. Here’s a breakdown of the key skills:
* Programming Languages: Proficiency in Python is non-negotiable.Experience with Java,C++,or Go is a significant plus.
* Cloud Computing: Deep understanding of at least one major cloud provider (AWS, Azure, GCP) and their AI/ML services. Cloud certifications are highly valued.
* Containerization & Orchestration: Expertise in docker and Kubernetes is critical for deploying and managing AI applications.
* Data Engineering: Familiarity with data pipelines, ETL processes, and data warehousing technologies (e.g., Spark, Hadoop, Snowflake).
* DevOps Practices: experience with CI/CD pipelines, infrastructure as code (IaC) (e.g., Terraform, cloudformation), and monitoring tools (e.g., Prometheus, Grafana).
* Machine Learning Fundamentals: A solid understanding of ML concepts, algorithms, and frameworks (e.g., TensorFlow, PyTorch, scikit-learn). You don’t need to build the models,but you need to understand how they work.
* Database Management: Experience with both SQL and NoSQL databases (e.g., PostgreSQL, MongoDB, Cassandra).
The Indiswork Advantage: What Sets Our Platform Engineering Team Apart
At Indiswork, our AI Platform Engineers are at the forefront of innovation. We offer:
* Cutting-Edge Projects: Work on challenging and impactful projects that leverage the latest advancements in artificial intelligence and machine learning.
* Collaborative Environment: A supportive and
