A Machine Learning Engineer II develops, tests, and deploys machine learning models, optimizing algorithms and data pipelines. They collaborate with data scientists and software engineers to enhance performance, analyze large datasets, and implement scalable solutions to solve complex business problems.

Job Role: Machine Learning Engineer II

Salary: ₹28 Lakhs to ₹70 Lakhs per year.

Location: Hyderabad, Telangana

Company: Uber

Qualification: Bachelor’s degree

About Company:

Uber is a global transportation and generation company centered in San Francisco, California. Founded in 2009, Uber revolutionized the trip-sharing industry with its mobile app, connecting passengers with drivers of vehicles for rent. The organization has increased its services to include meal delivery (Uber Eats) and freight transportation (Uber Freight). Uber operates in over 900 metropolitan regions worldwide, imparting a handy and green alternative to standard transportation techniques. Known for its disruptive innovation and controversies concerning exertion practices, Uber continues to form the future of urban mobility and virtual logistics on an international scale.

Role Overview:

Machine Learning Engineer II at Uber, Hyderabad, Telangana. Responsible for writing exceptional, green, and testable code, undertaking comprehensive code evaluations, and creating documentation. Designs powerful architectures aligned with Uber’s desires, executes responsibilities with urgency, and collaborates throughout groups. Promotes performance and fosters a tradition of excellence within Uber, leveraging talents in Java, Python, Go, JavaScript, and React. Bachelor’s in Computer Science or equivalent realistic experience required.

Roles and Responsibilities Of Machine Learning Engineer II:

Algorithm Development:

Design and implement gadget mastering algorithms and models to resolve complex troubles, specializing in scalability and overall performance.

Data Processing and Analysis:

Collect, preprocess, and analyze big datasets to extract meaningful patterns and insights, ensuring statistics are fine and integrity.

Model Training and Evaluation: Develop and train devices gaining knowledge of fashions through the use of frameworks like TensorFlow or PyTorch, compare version performance, and iterate to improve accuracy and performance.

Deployment and Integration:

Deploy models into manufacturing environments, collaborating intently with software engineers to combine system-gaining knowledge of solutions into scalable software systems or programs.

Research and Innovation:

Stay updated with the brand new improvements in machine studying artificial intelligence, and behavior research experiments, and advise revolutionary answers to beautify present models or expand new approaches.

Qualification:

Bachelor’s degree in Computer Science or related technical field or equivalent practical experience
Experience coding using general-purpose programming languages (eg. Java, Python, Go, Javascript, Fusion)

Skills Required For Machine Learning Engineer II:

Advanced Machine Learning Techniques:

Proficiency in advanced algorithms including deep learning, reinforcement studying, and herbal language processing (NLP). Experience with frameworks like TensorFlow or PyTorch is crucial.

Programming Languages:

Strong programming abilities in languages like Python, R, or Java for developing and imposing gadgets and getting to know fashions. Knowledge of records systems and algorithms is useful.

Statistical Analysis and Modeling:

Ability to use statistical methods and modeling strategies for big datasets. Understanding probability ideas and hypothesis checking out is crucial.

Data Engineering:

Skills in records preprocessing, function engineering, and pipeline development. Experience with gear like Spark for massive facts processing and SQL for facts querying is treasured.

Software Engineering:

Proficiency in software improvement practices which include version control (e.g., Git), code evaluation, and debugging. Familiarity with cloud platforms like AWS, Azure, or Google Cloud for deploying system mastering models.