Machine Learning Engineer

Senior Machine Learning Engineer – Biotech (Freelance Contract) Location: Remote or Hybrid Industry: Biotechnology / Life Sciences | Specialization: Machine Learning & MLOps Our organization is a rapidly expanding MLOps platform supporting data science, machine learning engineering, and computational biology teams within the biotechnology sector. We enable our partners to develop scalable, reproducible machine learning workflows that accelerate scientific research and operational decision-making. We are seeking a highly skilled Senior Machine Learning Engineer to lead the development, optimization, and deployment of advanced machine learning solutions across a variety of biological and scientific data streams. This role is offered on a freelance contract basis, with potential for extension. Key Responsibilities • Lead the design, development, and optimization of advanced ML and deep learning models for biological datasets, including genomics, imaging, and experimental data. • Build and automate robust, scalable data pipelines for both structured and unstructured biotech data. • Develop end-to-end ML workflows covering data preprocessing, model training, evaluation, deployment, and ongoing model lifecycle management. • Fine-tune and combine models using ensemble and other advanced techniques to maximize predictive accuracy. • Collaborate closely with engineering, computational biology, and product teams to translate scientific challenges into effective ML solutions. • Oversee the deployment, monitoring, and management of production ML systems, implementing industry-standard MLOps best practices. • Provide technical guidance and mentorship to junior team members and contribute to strategic planning. • Present analytical findings and model insights clearly to both technical and scientific stakeholders. Requirements • Strong proficiency in Python and SQL; familiarity with Scala, Java, or C++ is advantageous. • Extensive experience with TensorFlow, PyTorch, scikit-learn, and other modern ML frameworks. • Deep knowledge of MLOps, including Kubernetes, Docker, CI/CD, and scalable model deployment. • Demonstrated success in developing, deploying, and optimizing machine learning models in production environments, ideally within biotech, healthcare, or scientific domains. • Excellent analytical and mathematical capabilities, combined with strong problem-solving skills. • Effective communication skills, with the ability to work cross-functionally with scientific and engineering teams. • Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Data Science, Computational Biology, Bioinformatics, or a related field. • Prior freelance or contract-based experience managing technical projects independently is preferred. Preferred Qualifications • Experience with cloud platforms such as AWS, GCP, or Azure for ML deployment and data processing. • Background in NLP, computer vision, or multimodal learning, particularly applied to biological or clinical data. • Familiarity with scientific data formats (e.g., FASTQ, BAM, assay data, microscopy images). • Experience with data visualization frameworks for presenting scientific and analytical results.