Jan

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About
Jan is an open-source ChatGPT alternative that runs 100% offline. It allows users to chat with AI, download and run powerful models (like Llama3, Gemma, or Mistral), connect to cloud AIs (OpenAI, Groq, Cohere), set up a local OpenAI-compatible API server, and even chat with their files (experimental). The tool is highly customizable and offers features like assistants and memory (coming soon) and extensions for added functionality. It prioritizes user privacy and data ownership, storing everything locally in universal formats. Jan is developed by the Homebrew Computer Company, a team of AI researchers and engineers.
Platform
Task
Features
• chat with ai
• highly customizable
• extensions
• assistants & memory
• chat with your files
• local api server
• connect to cloud ais
• model hub
Job Opportunities
Mechatronics Engineer (Robotics)
Open-source ChatGPT alternative running 100% offline, prioritizing user privacy and data ownership.
Benefits:
We pay an “all-in” pay and you will cover your own insurance/medical from the amount
14 days leave (and unlimited sick days)
Annual equipment budget (once 2 month probation has been completed)
Experience Requirements:
Minimum 3 years of experience in mechatronics design and implementation
Proven experience working on robotics projects from concept to production
Expertise in CAD software (e.g. SolidWorks, Inventor), simulation tools (e.g. ISAAC SIM), and manufacturing processes
Strong background in electrical systems integration and control theory
Excellent analytical skills with the ability to troubleshoot complex issues and provide innovative solutions
Other Requirements:
Outstanding communication and collaboration skills to work effectively within a multidisciplinary team
Responsibilities:
Design and develop robotics systems, incorporating advanced sensors, actuators, and control algorithms
Collaborate with software engineers and AI experts to optimize system integration and performance
Provide technical leadership by mentoring junior engineers and contributing to company-wide projects as needed
Stay updated with industry trends, research new technologies, and identify opportunities for innovation within our products
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Sr. C/C++ Engineer (Hardware Acceleration)
Open-source ChatGPT alternative running 100% offline, prioritizing user privacy and data ownership.
Benefits:
We pay an “all-in” pay and you will cover your own insurance/medical from the amount
14 days leave (and unlimited sick days)
Annual equipment budget (once 2 month probation has been completed)
Experience Requirements:
Proven experience in C++ development, with a strong foundation in computer science fundamentals
Experience building C++ applications from the ground up, including proficiency in design patterns, data structures, and algorithms
Solid understanding of: High-performance computing principles and techniques, Hardware optimization strategies for embedded systems and GPUs (e.g., NVIDIA CUDA, OpenCL), Low-level programming languages such as Assembly or Rust
Minimum 3 years of professional work experience in a similar role
Self-starter with an entrepreneurial mindset and ability to work independently
Responsibilities:
Write kernels, or
Compilers, or
And low level APIs
Contribute towards important open source dependencies like Vulkan, OpenCL
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Sr. MLOps Engineer
Open-source ChatGPT alternative running 100% offline, prioritizing user privacy and data ownership.
Benefits:
We pay an “all-in” pay and you will cover your own insurance/medical from the amount
14 days leave (and unlimited sick days)
Annual equipment budget (once 2 month probation has been completed)
Experience Requirements:
Model Serving & Deployment: Proven Experience with Docker, Kubernetes (K8s), Seldon Core for containerization, orchestration and scaling of deployed models.
Infrastructure Management: Manage infrastructure using Terraform for Infrastructure-as-code
Deployment experience: to cloud environments (AWS, GCP, Azure) or Self hosted On-Premise environments
Pipeline Orchestration: Design and maintain data pipelines using Apache Airflow or Kubeflow for workflow automation
Experiment Tracking & Model Registry: Implement and manage MLflow for tracking experiments and maintaining a model registry
Responsibilities:
Deploy Models: Build and deploy machine learning models into Python based applications, ensuring they are accessible for real-time predictions.
Infrastructure Management: using Terraform or infrastructure as code.
Optimize Deployment Pipelines: Design CI/CD pipelines for seamless integration and deployment of machine learning models, utilizing tools like Docker and Kubernetes for containerization
API Integration: Create and maintain RESTful APIs or microservices to facilitate model serving and integration with existing applications.
Continuous Monitoring: Implement monitoring solutions to track model performance and detect issues such as data drift or performance degradation, ensuring timely updates.
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