Machine Learning Engineer

As a Machine Learning Engineer, you will be responsible for applying the latest developments in deep reinforcement learning to enhance the capabilities of autonomous systems. Your tasks will include creating OpenAI-Gym like environments for web and mobile applications, repurposing OpenAI and DeepMind RL algorithms, and formulating solutions for challenging RL problems in discrete action spaces. Additionally, you will utilize your expertise in Computer Vision, Image Processing, Image/Video Analytics, and Object Detection to prototype deep reinforcement learning and computer vision solutions. Your role will also involve collaborating with the team, including product owners, designers, and other developers, as well as customers to gain a comprehensive understanding of business and user requirements. You will be expected to build and evaluate algorithms for test optimization based on user behavior or statistical optimization, and create multi-agent systems to enhance project outcomes. To excel in this position, you should be an expert in Python and stay up to date with the latest advancements in reinforcement learning, particularly in OpenAI Gym, Self-Play, and Multiagent systems. Proficiency in deep learning frameworks such as PyTorch, Tensorflow, or Keras is essential, along with a proven track record of developing learning algorithms and/or systems. Experience in productionizing deep learning systems, pre-processing data sets for computer vision applications, and driving projects to completion with minimal guidance are crucial for success in this role. The ideal candidate will hold a Bachelor's or Master's degree in computer science, Mathematics, Machine Learning, or a related field, or possess equivalent experience. Strong communication skills, effective time management, and organizational abilities are also key qualifications required for this position. If you are a self-learner with a passion for acquiring new knowledge and skills, and possess the ability to communicate analysis results effectively, we encourage you to apply for this challenging and rewarding opportunity.,