Algorithm Engineer

About

Algorithm Engineer focus on developing, optimizing and productionizing computational methods for personalized healthcare, specifically DNA sequencing, digital pathology and image analysis.

Key Responsibilities

• Algorithm Development & Optimization: Design, implement and optimize AI/machine learning algorithms (e.g., Transformers, CNNs, Hidden Markov Models) for analyzing raw nanopore signal data and DNA sequencing (basecalling).
• Productionization (MLOps): Convert research code (Python/PyTorch/TensorFlow) into highly efficient, production-ready and scalable C++ or CUDA code for GPU cluster efficiency
• Pipeline Development: Architect distributed computing workflows and data pipelines using tools like Nextflow, Airflow and Docker/Singularity for high-performance computing (HPC) environments
• Data Analysis & Modeling: Apply computational methods to NGS datasets (genomics, transcriptomics) to identify biomarkers and develop diagnostic, predictive and classification models.
• Verification & Validation: Develop automated testing frameworks for digital pathology and imaging algorithms to ensure technical accuracy and performance.

Education

Typically a Master’s or PhD in Bioinformatics, Computer Science, Computational Biology, Electrical Engineering or a related field.

Technical Skills

• Languages: Strong proficiency in Python (including libraries like PyTorch/TensorFlow) and C++/CUDA.
• Platforms/Tools: Experience with Linux, Docker, Git and HPC workload managers like SLURM.
• Domain Expertise: Deep knowledge of algorithms for DNA analysis (e.g., dynamic programming, Hidden Markov Models) or digital pathology/image processing.

Experience

5+ years of industrial experience in software development, algorithm R&D or productionizing ML models.

Key Focus Areas

• Digital Pathology: Building image processing algorithms for disease diagnostics.