About the role

AI summarised

The Algorithm Engineer will design, develop, and optimize AI and signal processing algorithms for diagnostic systems at Thermo Fisher Scientific. They will collaborate with cross-functional teams to integrate algorithms into production systems, validate performance, and build scalable testing pipelines. The role requires strong programming skills in Python, Java, or Matlab, experience with ML frameworks, and the ability to work both independently and in teams.

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, hardware, and bioinformatics 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, Bioinformatics, Statistics or a related field; a Master’s degree is highly preferred
2+ years’ experience in algorithm and AI development and evaluation within a product development setting or SDLC experience
Strong programming skills in Python, Java, or Matlab for algorithm development
Familiar with signal processing, ML/AI models and statistical analysis (time-series or waveform data)
Familiar with ML frameworks TensorFlow, PyTorch, scikit-learn, or similar
Proven problem-solving capability and innovation mindset
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
Able to design and implement automated pipelines to streamline development, testing, and evaluation of AI/ML models and traditional algorithms
Hands-on experience of analyzing algorithm complexity and performance
Experience in biotechnology industry is a plus
Experience with Git, CI/CD, pytest/Junit
Experience integrating algorithms into instrument control or analysis software
Experience leveraging cloud infrastructure (AWS, Azure or GCP) and containerization frameworks for scalable solutions