Hello All, I am @salmon-flower-36598, VP Engineering at Pathomiq (http://www.pathomiq.com). We develop AI/ML models to detect cancer, using digital pathology. We are hiring for both MLOps and ML Engineers. Please see job descriptions below. All our positions are fully remote. Please reach out to me if interested. Pathomiq is an AI-powered precision oncology company developing diagnostic and prognostic tools from histopathology images. Our platform leverages advanced deep learning and unsupervised phenotyping to discover clinically relevant biomarkers and predict patient outcomes across multiple cancer types. By integrating spatial tissue morphology with genomic and transcriptomic data, we enable more informed treatment decisions and unlock novel insights into tumor biology. Pathomiq’s models are designed for transparency, scalability, and regulatory readiness—bridging the gap between cutting-edge research and clinical application. We are seeking a Machine Learning Engineer to support the deployment, automation, optimization, and scaling of machine learning models developed by our AI science team. In this role, you will be responsible for integrating models into production systems, improving performance, and ensuring reliability at scale. You will work closely with AI scientists, software engineers, and infrastructure teams to operationalize cutting-edge models. Responsibilities • Implement, optimize, and maintain machine learning models designed by AI scientists. • Contribute to the development of computer-vision based preprocessing algorithms. • Develop efficient data pipelines for model training, evaluation, and inference. • Integrate models into cloud-based or on-premises production environments. • Monitor model performance and proactively address issues like drift, latency, or scaling bottlenecks. • Work with DevOps and MLOps tools to automate model deployment, testing, and updates. • Collaborate with AI scientists to refine model outputs for production readiness. • Maintain model versioning, tracking, and reproducibility across environments. • Requirements • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. If holding a Bachelor’s degree, more industry experience will be expected. • Strong programming skills in Python and familiarity with ML frameworks like PyTorch. • Experience deploying machine learning models into production environments (REST • APIs, batch inference, streaming). • Familiarity with cloud platforms (AWS) and containerization (Docker, Kubernetes). • Understanding of model monitoring, retraining triggers, and A/B testing methodologies. • Strong problem-solving and communication skills. • Ability to collaborate effectively with research scientists and engineering teams. • Preferred Qualifications • Experience with MLOps tools and practices (MLflow, SageMaker, Vertex AI Pipelines). • Knowledge of performance optimization techniques (model quantization, pruning, GPU acceleration). • Experience with MLOps tools and practices (MLflow, SageMaker, Vertex AI Pipelines). • Proficient with data engineering tools (Pandas, SQL, Spark) and workflow orchestration (Flyte, Ray, etc.). • Knowledge of performance optimization techniques (model quantization, pruning, GPU acceleration).