Machine Learning Post Processing Engineer at Ralliant

Beaverton, Oregon, United States -

Full Time

Start Date

Immediate

Expiry Date

22 Feb, 26

Salary

0.0

Posted On

24 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Post Processing, GPU Programming, Python, C++, CUDA, ML Frameworks, Embedded Systems, Real-Time Processing, Data Pipelines, Model Optimization, Error Handling, Collaboration, AI Protocols, Containerization, Cloud Platforms

Industry

Electrical Equipment Manufacturing

Description

Design and implement post-processing pipelines for various ML model outputs (computer vision, NLP, time series, etc.) Develop efficient algorithms for model output refinement, filtering, and enhancement Build real-time and batch processing systems for ML inference results Optimize post-processing workflows for GPU-accelerated environments Create robust error handling and validation systems for ML pipeline outputs Implement model ensemble techniques and output aggregation strategies Design, implement, and maintain software for embedded systems, focusing on performance, reliability, and integration with broader system architectures Develop low-level drivers and firmware interfaces for GPU-enabled embedded platforms Collaborate with hardware teams to define embedded system specifications and constraints Build and support internal tools that improve developer productivity, streamline engineering workflows, and enhance operational processes across teams Develop GPU profiling and debugging tools for embedded and distributed systems Design monitoring and observability solutions for GPU resource utilization and performance Implement CI/CD pipelines optimized for ML post-processing development workflows Build developer experience tools that simplify ML pipeline development and deployment Collaborate on the development and integration of AI protocols, including Model Context Protocol (MCP), to enable intelligent system behavior and scalable machine learning infrastructure Implement GPU-optimized MCP servers for high-performance AI model serving and post-processing Design efficient data pipelines between GPU compute resources and MCP protocol endpoints Ensure seamless integration between GPU computing clusters and distributed AI protocol systems Develop secure communication protocols for GPU-accelerated AI model inference and post-processing Perform routine software development activities such as debugging, code reviews, documentation, and maintenance of non-AI components Implement robust errors in handling and logging for GPU-accelerated ML applications Collaborate with cross-functional teams on non-AI system components and integrations Developer Productivity & Operations Contribute to the design and implementation of retrieval-augmented generation (RAG) and related systems to improve contextual understanding and information access within intelligent applications Optimize GPU-accelerated vector databases and similarity search engines for RAG systems Implement efficient embedding generation and storage systems using GPU compute resources Design scalable knowledge retrieval architectures that leverage distributed GPU infrastructure Develop caching and indexing strategies for large-scale knowledge bases in GPU memory Collaborate on hybrid CPU-GPU architectures for balanced RAG system performance Integrate RAG capabilities with existing GPU-accelerated machine learning pipelines Master's degree or PhD in Computer Science, Electrical Engineering, Machine Learning, or related field 3-7 years of experience in software development with focus on ML systems or high-performance computing, or comparable work experience Strong programming skills in Python, C++, and CUDA Experience with ML frameworks (PyTorch, TensorFlow, JAX) and GPU programming Understanding of federated learning or distributed computing architectures Knowledge of computer vision, NLP, or other ML domains requiring post-processing Experience with data pipeline development and real-time processing systems Machine Learning & AI Deep understanding of ML model architectures and inference optimization Experience with model quantization, pruning, and optimization techniques Knowledge of ensemble methods and model output fusion strategies Familiarity with MLOps practices and model deployment pipelines Understanding of AI protocol standards and emerging technologies like MCP Experience with parallel algorithms and distributed computing frameworks Experience with embedded GPU platforms (NVIDIA Jetson, AMD Embedded, etc.) Experience with GPU cluster management and orchestration platforms Experience with vector databases and similarity search technologies Knowledge of information retrieval systems and search algorithms Experience with federated learning and distributed AI computation patterns Knowledge of edge computing and IoT device integration Background in signal processing or computer graphics Experience with containerization (Docker, Kubernetes) for ML workloads Familiarity with cloud platforms (AWS, GCP, Azure) and their ML services Understanding of security and privacy considerations in ML systems Experience with model serving architectures and API design patterns Knowledge of specialized hardware accelerators (TPUs, FPGAs, neuromorphic chips) Strong problem-solving abilities and analytical thinking Excellent communication skills for cross-functional collaboration Ability to work in fast-paced, research-oriented environments Strong attention to detail and commitment to code quality Collaborative mindset with ability to mentor junior engineers

Responsibilities

Design and implement post-processing pipelines for various ML model outputs and develop efficient algorithms for model output refinement. Collaborate with hardware teams and build internal tools to enhance operational processes across teams.