Intermediate Machine Learning Developer
Novarc Technologies Inc CA$114K–CA$167K/yr
Robotics Engineering · 51-200 employees
About the role
You will be responsible for building and deploying perception, classification, and AI capabilities for the NovAI intelligence suite. This includes developing models, integrating them with robotic hardware, and implementing MLOps practices for production pipelines.
What they look for
Requirements
Candidates must have a bachelor's degree in a relevant field and at least 3 years of practical experience with PyTorch, TensorFlow, and OpenCV. Proficiency in C++ and Python applied to robotic systems is required, along with strong analytical and problem-solving skills.
Full description
Role Summary:
Our never-before-seen, adaptive welding products push the limit of what physical AI in general, and machine learning in particular, can do today. We are looking for an experienced and hands-on Intermediate Machine Learning Developer to join our growing team and support existing products and help us develop new ones.
As an Intermediate Machine Learning Developer, reporting to the Development Manager, AI/Robotics, you will be responsible for building and deploying perception, classification, segmentation, and AI capabilities for our NovAI intelligence suite. Working with senior ML engineers, you will own the implementation of models and production pipelines!
Key Responsibilities :
- Model Development & Research
- Build, train, and evaluate computer vision/ML models (detection, segmentation, tracking, pose estimation, image fusion) using PyTorch and TensorFlow.
- Fine-tune neural network architectures (CNN, RNN, Transformer) and classical ML methods for real-time welding process sensing.
- Prototype and test new features against the applied research roadmap defined by senior engineering.
- Implement vision-language-action (VLA) models and foundation architectures for spatial reasoning and multi-modal perception.
- Develop and integrate world models for predictive perception, spatial reasoning, and dynamic environmental simulation in adaptive welding scenarios.
- Develop closed-loop reinforcement learning (RL), imitation learning, and continuous learning policies for complex control tasks, adaptive manipulation, and robotic feedback loops.
- Architect simulation-to-real (Sim2Real) pipelines and synthetic data workflows (Isaac Gym, MuJoCo) to accelerate domain randomization.
- Physical AI & Robotics Integration
- Fuse vision, arc, and thermal sensor data to enable real-time robotic decision-making.
- Integrate ML outputs with motion control, PLCs, and robot controllers within target latency, power, and compute budgets.
- Support toolpath and trajectory optimization for stress reduction, energy efficiency, and faster cycle times.
- Deployment, Testing & MLOps
- Optimize models for real-time edge implementation and maximize hardware efficiency using quantization, pruning, TensorRT, and ONNX Runtime on embedded platforms.
- Validate and troubleshoot end-to-end integrations across software and hardware stack components.
- Maintain state machine documentation, architecture diagrams, technical specifications, and user guides.
- Provide onsite and remote commissioning support during customer installations.
- Implement robust MLOps practices, including automated model versioning, validation workflows, and monitoring (W&B/MLflow).
- Cross-Functional Collaboration
- Partner with controls and welding engineering teams to integrate robotic systems with external automation hardware.
- Contribute to scientific publications and patent applications; evaluate emerging ML tooling and frameworks.
- Travel as required to customer deployment sites or industry conferences.
Qualifications and Experience :
- Bachelor's degree in CS, Electrical Engineering, Software Engineering, or related field (Master's preferred).
- 3+ years of practical experience with PyTorch, TensorFlow, and OpenCV.
- Strong foundation in supervised/unsupervised learning, deep neural networks (CNNs/RNNs), and transfer learning.
- Solid computer vision skills: image processing, object detection, segmentation, and feature extraction.
- Proficiency in C++ and Python applied to robotic systems.
- Strong communication and documentation skills
- Strong organizational and time management skills
- Strategic, critical thinking, and analytical skills for structured problem-solving rather than band-aid fixes.
Preferred Additional Qualifications:
- Experience integrating an AI pipeline with a third party PC/PLC
- Hands-on exposure to edge inference optimization or Physical AI systems.
- Familiarity with industrial sensing or automated welding processes.
Commitment to Novarc Values:
Demonstrate and embed Novarc’s core values of Hungry, Humble, and Smart in daily work, decision-making, collaboration, and interactions with colleagues, customers, and partners.
- Hungry: Be Passionate, Never Give Up, Customer Focused, Continuous Learning, Do What You Say
- Humble: Servant Leader, No arrogance, Less Ego, Listen > Talk
- Smart: High EQ, Challenge the Status Quo, Thought Leaders
Similar roles
-
Applied Machine Learning Engineer - Localization
Apple Cupertino, California, United States
-
Machine Learning Scientist, Multimodal AI
Natera United States · $125K–$172K/yr
-
Staff Applied Machine Learning Engineer - Intelligent Data, Signals & Systems
Block California, United States · $277K–$415K/yr
-
Machine Learning Engineer - Health AIML
Apple Cupertino, California, United States
-
Senior Staff Machine Learning Engineer, Agentic Systems - Moveworks
ServiceNow Mountain View, California, United States
-
Post-Doctoral Research Associate: Computational Materials Science and Machine Learning - UTK
University of Tennessee Knoxville, Iowa, United States · $70K–$75K/yr