Historica
Work format
remote
Work type
fulltime
Grade
middle/senior
1 week ago
ML/DS Engineer
machine learning
deep learning
python
sql
tensorflow
pytorch
scikit-learn
mlops
#vacancy #remote
ML/DS Engineer (Full-Time / Part-Time)
At Historica, we are building an interactive application for exploring history through a dynamic map. We need a highly skilled ML/DS Engineer to develop AI-driven solutions that analyze and process historical data - covering topics from politics and religion to epidemics - and to build recommendation systems and boundary-generation models.
Key Responsibilities
- Research, design, and implement machine learning solutions (both classic ML and deep learning) with a focus on geospatial tasks (e.g., boundary generation, segmentation of historical maps).
- Continuously integrate emerging ML/DS/AI techniques (CNN, RNN, GAN, etc.) into our platform, applying them to historical boundary generation and analysis of spatial-temporal data.
- Collaborate closely with the team to deliver reliable and scalable models.
- Contribute to data pipelines, ensuring high-quality data management and MLOps practices.
- Innovate and maintain state-of-the-art solutions for user-facing features (historical question-answering, recommendation engines).
Requirements
- 3+ years of experience in machine learning engineering (classic ML and deep learning).
- Strong Python, SQL, TensorFlow/PyTorch, scikit-learn skills; familiarity with LLM/RAG and NLU.
- Hands-on experience with ML research (Arxiv publications are a plus).
Sound knowledge of data structures, algorithms, and software design principles.
- Familiarity with cloud platforms (AWS/Google Cloud) is preferred.
- Bachelor’s/Master’s degree in Computer Science, Engineering, Mathematics, or related field.
- DevOps/MLOps (CI/CD, containerization) experience is a plus.
ML/DS Engineer (Full-Time / Part-Time)
At Historica, we are building an interactive application for exploring history through a dynamic map. We need a highly skilled ML/DS Engineer to develop AI-driven solutions that analyze and process historical data - covering topics from politics and religion to epidemics - and to build recommendation systems and boundary-generation models.
Key Responsibilities
- Research, design, and implement machine learning solutions (both classic ML and deep learning) with a focus on geospatial tasks (e.g., boundary generation, segmentation of historical maps).
- Continuously integrate emerging ML/DS/AI techniques (CNN, RNN, GAN, etc.) into our platform, applying them to historical boundary generation and analysis of spatial-temporal data.
- Collaborate closely with the team to deliver reliable and scalable models.
- Contribute to data pipelines, ensuring high-quality data management and MLOps practices.
- Innovate and maintain state-of-the-art solutions for user-facing features (historical question-answering, recommendation engines).
Requirements
- 3+ years of experience in machine learning engineering (classic ML and deep learning).
- Strong Python, SQL, TensorFlow/PyTorch, scikit-learn skills; familiarity with LLM/RAG and NLU.
- Hands-on experience with ML research (Arxiv publications are a plus).
Sound knowledge of data structures, algorithms, and software design principles.
- Familiarity with cloud platforms (AWS/Google Cloud) is preferred.
- Bachelor’s/Master’s degree in Computer Science, Engineering, Mathematics, or related field.
- DevOps/MLOps (CI/CD, containerization) experience is a plus.