Algorithm Engineer

Merck Merck · Pharma · North Central District, Israel

This role focuses on designing and optimizing signal processing and machine learning algorithms for animal monitoring systems, specifically for constrained edge environments. The engineer will develop, train, evaluate, and optimize models, ensuring their integration into production systems, and writing production-quality code.

What you'd actually do

  1. Translate real-world sensing challenges into robust algorithmic solutions
  2. Develop and optimize signal processing and embedded ML algorithms for constrained edge environments
  3. Analyze datasets, evaluate model performance, and iterate to improve accuracy and robustness
  4. Collaborate closely with hardware, firmware, and software teams to integrate algorithms into production systems
  5. Establish best practices for experimentation, validation, and reproducibility

Skills

Required

  • Algorithm Design
  • Algorithms
  • Artificial Intelligence (AI)
  • Communication
  • Debugging
  • MATLAB
  • Multidisciplinary Collaboration
  • Python (Programming Language)
  • signal processing
  • embedded ML
  • training models
  • evaluating models
  • tuning models
  • optimizing models
  • resource constraints
  • software engineering best practices
  • GIT
  • code reviews
  • automated testing
  • CI/CD
  • C/C++

Nice to have

  • MSc
  • IoT
  • sensor-based systems
  • TinyML
  • model quantization
  • A/B testing
  • field validation frameworks
  • animal health
  • agriculture domains
  • technical leadership
  • Computer Vision
  • Data Engineering
  • Data Modeling
  • Data Science
  • Data Visualization
  • High Performance Computing (HPC)
  • Machine Learning (ML)
  • Real-Time Programming
  • Software Development

What the JD emphasized

  • embedded ML
  • resource constraints
  • optimizing models

Other signals

  • Develop and optimize signal processing and embedded ML algorithms for constrained edge environments
  • Analyze datasets, evaluate model performance, and iterate to improve accuracy and robustness
  • Experience optimizing models under resource constraints (CPU, memory, power)