Learn about and apply for artificial intelligence careers in the United States

Artificial intelligence (AI) is one of the most rapidly growing and in-demand fields in the United States, with job openings expected to grow 33% by 2030, much faster than the average for all occupations. AI careers offer competitive salaries, exciting opportunities to work on cutting-edge technologies, and the chance to make a real impact on the world. In this article let’s explore the available AI careers in the United States.

1. NTT Data

Role: AI/ML Developer

Qualifications: Expertise in Python libraries for machine learning, natural language processing, image preprocessing, and databases, as well as experience with machine learning toolkits and frameworks, deep learning concepts, and applying ML algorithms effectively.

Responsibilities: Design, develop, and train machine learning systems to optimize performance and build self-learning applications. Stay up-to-date with the latest developments in the field and use GPU, pyspark, and parallel compute libraries in Python. Understand how components and processes work together using library calls, REST APIs, queueing/messaging systems, and database queries. Design systems to avoid bottlenecks and scale well with increasing volumes of data.

Link to apply

2. State Street Corporation

Role: AI/ML Engineer

Qualifications: Data science, statistics, or machine learning education, with strong programming skills in Python, R, or Java. Experience with machine learning frameworks and setting up machine learning problem spaces and solutions. Ability to evaluate the performance and efficacy of machine learning solutions and interest in advanced machine learning concepts.

Responsibilities: Collaborate with data scientists, machine learning engineers, software engineers, and QA engineers to explore and prototype data and machine learning opportunities. Design, develop, test, and support services to deploy resulting models and cognitive solutions in production.

Link to apply

3. Apple

Role: Machine Learning Researcher

Qualifications: 3+ years of experience in machine learning for computer vision applications, with a strong understanding of machine learning algorithms and a proven track record of developing and deploying high-quality computer vision algorithms. Excellent software design, problem-solving, and debugging skills. Fluency in Python and another language (C/C++, Go, Rust), as well as experience with relevant deep learning software packages. Great technical skills, a drive for high-quality software, and the ability to innovate creative solutions. Excellent communication and the flexibility to learn new technologies.

Responsibilities: You’ll be involved in all stages of model development, from data analysis and prototyping to testing and deployment.

Link to apply

4. Intel

Role: AI Research Scientist

Qualifications: Masters degree in Electrical Engineering, Computer Engineering, Computer Science, or related field, with 3+ years of experience in neural architecture search (NAS) and machine learning for training deep neural networks.

Responsibilities: This role is available as a fully home-based and generally would require you to attend Intel sites only occasionally based on their business need. This role may also be available as our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site. In certain circumstances, the work model may change to accommodate business needs.

Link to apply

5. Genentech

Role: Senior Artificial Intelligence Scientist

Qualifications: Experience in machine learning research, evidenced by at least one first-author publication in a scientific journal, or equivalent, and experience with search and/or optimization-based sequence design algorithms.

Responsibilities: You will join a growing team of AI/ML experts and work closely with scientists across gRED to develop and deploy machine learning methods for the analysis of single-cell genomics datasets and biological sequences. You will help build and scale machine learning techniques to handle massive datasets and deploy novel machine learning algorithms.

Link to apply