About the role

AI summarised

The Senior Algorithm and ML Engineer designs, develops, and optimizes machine learning and signal processing algorithms for diagnostic systems at Thermo Fisher Scientific. This role involves building scalable testing pipelines, collaborating with cross-functional teams, and ensuring algorithm reliability and integration into production environments. The position requires strong programming skills, expertise in ML frameworks, and experience in product development settings.

Life SciencesOnsite

Key Responsibilities

Design, develop, and optimize signal processing, machine learning, and AI algorithms across various domains
Evaluate and validate algorithm performance using simulations, real-world datasets and automated testing frameworks
Analyze algorithm behavior under various conditions, identify limitations, and provide actionable feedback for improvement
Build scalable pipeline and frameworks to automate algorithm testing and streamline model development workflows
Collaborate with cross-functional teams, including software, firmware, hardware, and system team, to ensure algorithms meet product requirements and are well-integrated into production systems
Contribute to code quality, reproducibility, and documentation for algorithm reliability and transparency
Stay ahead of emerging technologies and industry standards for genetic science algorithm innovation

Requirements

BS in Computer Sciences or Computer Engineering, Mathematics, or a related field
Master’s degree is highly preferred
3-5 years’ experience in algorithm and AI development and evaluation within a product development setting or SDLC experience
Strong programming skills in C/C++, Java, or Python for algorithm development
Expertise in signal processing, Computer Vision, ML/AI models and statistical analysis
Hands-on experience of analyzing algorithm complexity and performance
Familiar with ML frameworks TensorFlow, PyTorch, scikit-learn, or similar
Proven problem-solving capability and innovation mindset
Able to design and implement automated pipelines to streamline development, testing, and evaluation of AI/ML models and traditional algorithms
Strong communication skills, and the ability to present work to both technical experts and non-experts
Ability to work both independently and collaboratively
Ability to handle multiple tasks and prioritize tasks effectively