Machine Learning (ML) Engineer
Role details
Job location
Tech stack
Job description
We're seeking a motivated ML Engineer to help advance our AutoML platform. You'll play a key role in expanding its capabilities, onboarding new ML use-cases across vision, time-series, and beyond, and improving the product as we scale. This role offers meaningful growth potential toward a technical leadership track., * Contribute to the development and enhancement of our AutoML system for Edge AI, including pipelines that combine deep-learning and conventional algorithms for embedded devices
- Object tracking, multi-model pipelines, and emerging use-cases
- Build and improve platform features across compute clusters and our web application
- Define abstractions and contribute to the architecture of cloud, cluster, and embedded components
ML Use-Case Expansion
- Integrate new ML use-cases across a broad range of data domains and maintain and improve existing ones, including:
- Time-series and audio, object re-identification, segmentation and keypoints
- Action recognition (video), radar and point cloud data, multi-modal (vision + audio + sensor)
- Small language models (NLP/SLM), classification, and object detection
- Work with foundational computer vision and non-CV ML models - train, evaluate, modify, and combine them to unlock new functionality
Edge AI Optimization & Deployment
- Optimize AI solutions for edge devices using TinyML frameworks, creating models that fit a range of chip sizes and memory constraints
- Deploy ML and non-ML algorithms on embedded targets (MCU and application-class microprocessors)
- Productize research-quality code into robust, production-ready systems
Collaboration & Craft
- Partner on data strategies, preprocessing pipelines, and model training workflows
- Stay current with Edge AI and AutoML advancements
- Document your work and contribute to technical reports
Requirements
- Master's degree in CS, EE, or a related field (PhD a plus)
- 4+ years of relevant industry experience in ML (AutoML and Edge AI experience highly valued)
- Strong Python skills with the ability to write production-quality code; C/C++ a plus
- Solid command of ML frameworks: TensorFlow, PyTorch, ONNX
- Proficient with the standard DS toolset: scikit-learn, OpenCV, pandas
- Comfortable working in Linux-based development environments
- Experience onboarding new ML use-cases and expanding into new data domains
- Excellent problem-solving skills and strong written and verbal English communication
Preferred
- Experience with cloud platforms (AWS) and web technologies (Node.js, REST APIs)
- Familiarity with compute cluster tools such as Ray and Optuna
- Knowledge of model compression techniques: pruning, quantization, transfer learning, knowledge distillation
- Experience defining software architecture for ML systems
- Familiarity with CI/CD practices
- Understanding of embedded systems concepts
- Experience with non-ML algorithms and signal processing
Benefits & conditions
- Market Opportunity - Edge AI is exploding, and we're solving a critical pain point in a massive and growing market
- Real Customer Impact - Our platform compresses 12-24 months of model development into 24-48 hours - validated by customers like NXP and Silicon Labs
- Technical Depth - Work on hard, meaningful problems at the intersection of AutoML, TinyML, and embedded systems
- Growth Trajectory - Join during a pivotal growth phase with significant room to grow into technical leadership
- Competitive Compensation - Base salary, performance-based bonus, and meaningful equity stake
ModelCat is an equal opportunity employer committed to building a diverse and inclusive team.