Principal Applied Machine Learning Systems Engineer Clone

## About the Company LuxTronic builds AI-powered inspection and production monitoring systems to help manufacturers run smarter. ## About the Role We are seeking a Principal Applied Machine Learning & Systems Engineer to design, deploy, and operate production-grade ML systems in real industrial environments. This role spans edge, on-prem, and cloud ML, where reliability, latency, and uptime matter more than offline benchmarks. At the Principal level, you will define architecture, technical standards, and long-term ML strategy across deployments. This role is deeply hands-on and requires comfort working under real-world constraints such as sensor noise, environmental variability, and mission-critical uptime. Location: Remote (Mountain Time preferred) Schedule: Flexible hours; ~50–60 hours/week Travel: Quarterly on-site visits to industrial facilities (factories, plants) ## Responsibilities ### Model Development & Deployment * Design and implement ML models for industrial use cases, including predictive maintenance, anomaly detection, quality inspection, and process optimization. * Build models resilient to noisy, incomplete, and high-variance industrial data. * Develop using modern ML frameworks (PyTorch, TensorFlow, ONNX) and deploy across: * Edge and embedded systems * On-prem industrial servers * Cloud and hybrid infrastructure * Validate model performance under operational constraints (latency, vibration, temperature, sensor drift). * Implement fail-safes, fallback logic, and degradation strategies for mission-critical systems. ### Production Engineering & Infrastructure * Deploy ML systems using Docker, Kubernetes, and CI/CD pipelines (GitHub Actions). * Build and maintain real-time and batch inference pipelines with strict reliability and latency requirements. * Integrate ML services with industrial control systems (PLCs, SCADA, edge controllers). * Develop secure, low-latency APIs to enable ML integration in industrial environments. ### Optimization for Industrial Constraints * Optimize models for performance and efficiency using quantization, pruning, and edge-optimized inference runtimes. * Balance accuracy, throughput, and resource constraints across heterogeneous hardware. * Ensure sub-second decision-making where required by industrial processes. ### Monitoring, Reliability & Troubleshooting * Monitor deployed models for drift, degradation, and infrastructure issues. * Build dashboards and alerts using Grafana or similar tools. * Troubleshoot live production issues involving hardware, networking, data quality, and model behavior with minimal operational impact. ### Collaboration & On-Site Work * Partner with industrial engineers and operations teams to translate factory requirements into ML solutions. * Participate in quarterly on-site visits to assess deployment environments and optimize systems in place. ### Extreme Ownership * Own ML systems from design through long-term operation. * Anticipate failure modes and proactively mitigate risk. * Deliver high-quality outcomes under real-world constraints and tight timelines. ### Principal Level Additional Responsibilities * Define ML architecture and deployment patterns across multiple industrial sites. * Establish best practices for model lifecycle management, deployment, and monitoring. * Lead technical tradeoffs between accuracy, latency, reliability, and cost. * Review designs and implementations across multiple ML initiatives. * Mentor senior engineers and raise overall engineering standards. * Act as technical authority during high-severity production incidents. * Other duties as assigned. ## Qualifications * Strong Python expertise for ML and production systems. * Deep experience with ML frameworks (PyTorch, TensorFlow, ONNX). * Proven experience deploying ML in industrial, edge, or embedded environments. * Experience with Docker, CI/CD pipelines, and GitHub Actions. * Proficiency with Ubuntu/Linux and Bash scripting. * Experience building APIs using AWS services (API Gateway, Lambda, SageMaker). * Familiarity with industrial protocols (Modbus, OPC UA) and factory systems (PLCs, SCADA). * Experience monitoring production systems using Grafana or similar tools. * Strong real-time debugging and problem-solving skills. * Willingness to travel quarterly and sustain a demanding workload. ## Required Skills * AWS IoT Core / Greengrass experience. * Edge inference optimization (TensorRT, OpenVINO, Jetson). * Prior experience in manufacturing, robotics, or industrial automation. ## Preferred Skills * Production-first * Edge-aware * Reliability-driven * Hands-on and accountable ## Compensation & Benefits This position offers an attractive compensation package consisting of a competitive salary, equity, and bonus opportunity. Luxtronic currently offers employer-paid base plans of 85-90% for Medical, Dental, Vision, Long-Term Disability, and Life Insurance. ## Working Conditions/Physical Demands The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions. While performing the duties of this job, the employee is frequently required to stand, walk, use hands and fingers, handle or feel, reach with hands and arms, climb or balance, stoop, kneel, crouch, or crawl and talk and hear. The employee must lift and/or move at least 45 pounds. Specific vision abilities required by this job include close vision, peripheral vision, depth perception and ability to adjust focus, and the ability to accurately see and label color. ## About the Company Luxtronic is an Equal Opportunity Employer