Актуальные заказы по Machine Learning Engineer
Lead ML Engineer
Responsibilities
• Evaluate and adapt state-of-the-art machine learning (ML), computer vision (CV), generative AI, and time series forecasting algorithms to meet product and client objectives.
• Research, design, and implement innovative ML algorithms for image, video, multimodal, and temporal data.
• Architect and develop full-stack ML pipelines—from data acquisition and preprocessing to training, evaluation, and deployment in cloud (AWS) or edge environments.
• Prototype and validate proof-of-concept (POC) solutions for vision, generative AI, and time-series forecasting problems.
• Translate customer requirements into actionable tasks, ensuring a clear understanding of objectives, scope, and expected outcomes.
• Analyze structured and unstructured data to uncover trends, patterns, and anomalies. Apply ML and statistical methods for prediction and forecasting.
• Prepare detailed technical documentation, reports, and presentations for internal and external stakeholders.
• Communicate complex technical topics effectively to both technical and non-technical stakeholders, including clients and business partners.
• Lead projects from prototype to production, ensuring scalability, reliability, and performance of solutions.
• Contribute to internal software development processes and team collaboration initiatives.
Requirements
• Strong hands-on experience in delivering ML solutions, including production-grade computer vision and forecasting models.
• Proven expertise in forecasting and time series data handling (e.g., ARIMA, LSTM, temporal convolutional networks).
• Proficiency in image and video processing, including segmentation, pose estimation, object detection, and multimodal data fusion.
• Experience with generative AI models such as diffusion-based text-to-image/video, multimodal LLMs, and prompt engineering.
• Skilled in reading, interpreting, and applying insights from academic research papers.
• Expertise in deep learning frameworks like PyTorch or TensorFlow.
• Strong object-oriented programming skills with clean, production-quality Python code.
• Familiarity with Vision Transformers (ViTs), especially for action recognition, object tracking, and video understanding tasks.
• Cloud deployment experience, particularly with AWS.
• Excellent communication skills in English (C1 or higher), both written and spoken.
• Strong ability to work independently, prioritize tasks, and manage multiple projects simultaneously.
Nice to Have
• Master’s or Ph.D. degree in Machine Learning, Computer Science, Mathematics, or a related field.
• Contributions to open-source ML or CV libraries or participation in Kaggle competitions.
Software Engineer
Task: Test SDK (software development kit) by documentation, run examples, write a report on what was hard/normal/easy, give recommendations for improvement.
Requirements:
- Proficiency in Python, with a solid understanding of object-oriented programming principles.
- Experience working with Linux operating systems, advanced CLI user.
- Proficiency in Bash scripting for automation and task management.
- Experience with Git for version control and collaborative development.
- Experience with Docker for containerization and deployment of applications.
- Hands-on experience running Large Language Models (LLMs) on-premise.
- Proficiency in utilizing NVIDIA GPUs to accelerate model inference and training processes.
Preferred Qualifications:
- Experience with NVIDIA’s TensorRT-LLM or similar frameworks to optimize and deploy LLMs efficiently.
- Familiarity with Kubernetes for orchestrating containerized applications in a clustered environment.
Additional Technical Skills:
- Familiarity with machine learning frameworks such as PyTorch.
- Understanding basic modern ML and DL concepts and Neural Networks architectures.