Machine Learning (ML) Engineer - Applied

Full-time
Europe
Mid Level
Posted 2 hours ago
Apply for this position →

ModelCat  |  Remote from Europe

 

About ModelCat

ModelCat is transforming how companies develop AI models for embedded, edge, and IoT devices. Our innovative platform uses AI to build AI — turning model architecture selection, training, optimization, and validation into a single powerful step.

ModelCat takes what was previously a 12–24 month process requiring highly skilled AI professionals and reduces it to a 24–48 hour AI-powered job that can be run by developers, data scientists, and product owners. Trusted by industry leaders like NXP and Silicon Labs, ModelCat is a venture-backed startup headquartered in Sunnyvale, California.

 

The Role

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.

 

What You'll Do

 

AutoML Platform Development

  • 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

 

Who You Are

Required

  • 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

Mindset

  • Proactive, entrepreneurial approach — you thrive with ownership and ambiguity

  • Startup mentality: you move fast, learn faster, and care deeply about the outcome

Why Join ModelCat

  • 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.

 

Apply for this position →

Machine Learning (ML) Engineer - Applied

ModelCat  |  Remote from Europe

 

About ModelCat

ModelCat is transforming how companies develop AI models for embedded, edge, and IoT devices. Our innovative platform uses AI to build AI — turning model architecture selection, training, optimization, and validation into a single powerful step.

ModelCat takes what was previously a 12–24 month process requiring highly skilled AI professionals and reduces it to a 24–48 hour AI-powered job that can be run by developers, data scientists, and product owners. Trusted by industry leaders like NXP and Silicon Labs, ModelCat is a venture-backed startup headquartered in Sunnyvale, California.

 

The Role

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.

 

What You'll Do

 

AutoML Platform Development

  • 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

 

Who You Are

Required

  • 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

Mindset

  • Proactive, entrepreneurial approach — you thrive with ownership and ambiguity

  • Startup mentality: you move fast, learn faster, and care deeply about the outcome

Why Join ModelCat

  • 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.