Junior Machine Learning Engineer (+бронювання)

We are currently looking for a Junior Machine Learning Engineer to join a technology company specializing in navigation hardware for aerial vehicles. This is a fully remote position.

The company fosters a fast-paced startup culture with direct communication, a flat hierarchy, and minimal bureaucracy. You will work with people who value the Growth Mindset, embrace challenges, aren’t afraid to learn from mistakes, and focus on finding solutions rather than pointing fingers.

The company actively integrates automation and AI technologies into all company processes to be 10x more efficient, reduce routines, and free up time for creative jobs.

Key responsibilities:

• Perform data analysis to evaluate data quality, identify trends, and ensure suitability for machine learning experiments.
• Prepare and preprocess datasets, ensuring best practices for model generalization and robustness in ML models.
• Prototype and optimize data processing pipelines, collaborating with the DevOps team for seamless deployment.
• Design, train, and evaluate ML models in collaboration with senior engineers.
• Analyze experiment results, identify potential pitfalls, and propose improvements for model performance.

• Proactively communicate key findings, intermediate analyses, and experiment results to stakeholders and partners, translating insights into actionable recommendations.

   

Required skills:

• Upper-intermediate, preferably Advanced English.
• Master’s or PhD degree in Computer Science, Machine Learning, Robotics, or a related field.
• 1-2 years of commercial experience in Machine Learning, Robotics, or a related field.
• Solid command of Machine Learning, Statistics, and Signal Analysis.
• Hands-on experience with Python and ML frameworks such as TensorFlow or PyTorch, SciPy, and Scikit-learn.
• Strong understanding of data preprocessing, feature engineering, and model evaluation.
• Familiarity with supervised and unsupervised learning techniques applied to sensor or image data.

• Basic understanding of software engineering principles, including version control (Git) and containerization (Docker).

   

Preferred qualifications:

• Command of at least one other language for data analysis, such as R and Scala.
• Familiarity with the Spark data analysis framework.
• Exposure to robotics, reinforcement learning, SLAM (Simultaneous Localization and Mapping), or sensor fusion.
• Experience working with real-time sensor data (e.g., LiDAR, camera, IMU).

• Understanding of cloud-based ML model deployment and optimization techniques.

   

Note! When possible, add a link to your GitHub and any code examples with your application.