Senior Software Developer, ML Engineer

• Develop and optimize machine learning models for deployment on resource-constrained devices (edge computing, smartphones, and embedded systems)
• Convert, compress, and quantize models (TensorFlow Lite, ExecuTorch, ONNX Runtime) to ensure low latency and efficient memory usage
• Perform extraction, transformation, and analysis of data from mobile application logs and propose new data collection if necessary
• Implement MLOps pipelines for versioning, validation, and continuous deployment of embedded models
• Collaborate with mobile development and framework teams to integrate predictive models into production
• Monitor data and concept drift in deployed models, proposing retraining and updates

Requirements

• Proven experience (2 to 4 years) in data science with a focus on applied machine learning
• Solid programming fundamentals, including data structures, algorithms, Git version control, and writing clean code
• Proficiency in Python and libraries such as NumPy, Pandas, Scikit-learn, and TensorFlow/PyTorch
• Experience with at least one embedded model format: ONNX, TensorFlow Lite, or ExecuTorch
• Good understanding of embedded systems and hardware constraints (memory, CPU, battery)
• Degree in Computer Science, Computer Engineering, Electrical Engineering with emphasis on embedded systems, Data Science, or related fields
• Advanced English for reading, writing, and conversation
• Familiarity with mobile development (Android/Kotlin) to support integration is a plus
• Practical knowledge of model optimization for the edge (pruning, quantization, knowledge distillation) is a plus
• Experience with MLOps frameworks (Kubeflow, MLflow, DVC) is a plus
• Knowledge of signal processing (audio, accelerometer, gyroscope) for mobile device models is a plus
• Familiarity with deployment across heterogeneous environments (ARM, mobile GPU, DSP) is a plus
• Master's degree or postgraduate studies in ML/AI will be considered a plus

Core Competencies

Demonstrates expertise in developing and optimizing machine learning models for resource-constrained devices, with a strong focus on MLOps practices and model optimization techniques. Proficient in Python and familiar with embedded systems, ensuring efficient integration and deployment of predictive models.

Highest-signal resume keywords

• Machine Learning Model Development
• MLOps Implementation
• Python Programming
• Embedded Systems Knowledge
• Model Optimization Techniques

ATS Optimization Keywords

Hard Skills

• Machine Learning
• Data Science
• Model Compression
• Quantization
• TensorFlow Lite
• ExecuTorch
• ONNX Runtime
• Data Structures
• Algorithms
• Git Version Control

Soft Skills

• Collaboration
• Communication

Certifications & Qualifications

• Master's Degree in ML/AI

Industry Keywords

• Edge Computing
• Mobile Development
• Data Transformation
• Concept Drift Monitoring
• Signal Processing

Tools & Technologies

• NumPy
• Pandas
• Scikit-learn
• TensorFlow
• PyTorch
• Kubeflow
• MLflow
• DVC