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Trainee Edge Ai Machine Learning Jobs (NOW HIRING)

BeeGenius is building the future of work, and they are seeking an AI/Machine Learning Engineer to join their team. In this role, you will be responsible for developing and implementing machine ...

Engineer II, AI/Machine Learning

Irvine, CA · On-site

$103K - $141K/yr

The AI/Machine Learning Engineer II will analyze data from various sources to develop computational ... the cutting edge of technology through the generation of patentable ideas • Effectively ...

The AI/Machine Learning Engineer II will be part of the R&D team at Masimo with focus on design and ... It is a cutting-edge research and development opportunity with the potential to improve people ...

The AI/Machine Learning Engineer II will be part of the R&D team at Masimo with focus on design and ... It is a cutting-edge research and development opportunity with the potential to improve people ...

As an Applied Machine Learning Engineer, you will serve as a vital bridge between cutting-edge AI research and practical, real-world applications. Your work will focus on developing, fine-tuning, and ...

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Trainee Edge Ai Machine Learning information

What is the difference between Trainee Edge Ai Machine Learning vs Data Analyst?

AspectTrainee Edge Ai Machine LearningData Analyst
Required CredentialsBasic programming, introductory AI/ML coursesStatistics, data visualization, Excel, SQL
Work EnvironmentTech companies, AI startups, R&D labsBusiness, finance, marketing departments
Industry UsageDeveloping AI models, machine learning pipelinesInterpreting data, generating reports

While both roles involve working with data, Trainee Edge Ai Machine Learning focuses on developing and training AI models, requiring programming and machine learning knowledge. Data Analysts primarily interpret existing data to inform business decisions, emphasizing statistical analysis and visualization skills. The roles differ in technical depth and focus but often overlap in data handling and industry usage.

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Infographic showing various Trainee Edge Ai Machine Learning job openings in the United States as of July 2026, with employment types broken down into 67% Full Time, 22% Part Time, and 11% Contract. Highlights an 59% Physical, 3% Hybrid, and 38% Remote job distribution.

Applied Machine Learning Engineer

Fireworks AI

New York, NY

Other

Posted 10 days ago


Job description

The Role:

As an Applied Machine Learning Engineer, you will serve as a vital bridge between cutting-edge AI research and practical, real-world applications. Your work will focus on developing, fine-tuning, and operationalizing machine learning models that drive business value and enhance user experiences. This is a hands-on engineering role that combines deep technical expertise with a strong customer focus to deliver scalable AI solutions.

Key Responsibilities:
  • Customer Success: Collaborate directly with the GTM team (Account Executives and Solutions Architects) to ensure smooth integration and successful deployment of ML solutions.
  • Demo / Proof of Concept (PoC): Build and present compelling PoCs that demonstrate the capabilities of our AI technology.
  • Application Build: Design, develop, and deploy end-to-end AI-powered applications tailored to customer needs.
  • Platform Features / Bug Fixes: Contribute to the internal ML platform, including adding features and resolving issues.
  • New Model Enablements: Integrate and enable new machine learning models into the existing platform or client environments.
  • Performance Optimizations: Improve system performance, efficiency, and scalability of deployed models and applications.
  • Partnership Enablement: Work closely with partners to enable joint AI solutions and ensure seamless collaboration.
Minimum Qualifications:
  • Bachelor's degree in Computer Science, Engineering, or a related technical field.
  • 5+ years of experience in a software engineering role, with a strong preference for customer-facing roles.
  • Robust coding skills required, preferably with proficiency in Python.
  • Demonstrated ability to lead and execute complex technical projects with a focus on customer success.
  • Strong interpersonal and communication skills; ability to thrive in dynamic, cross-functional teams.
Preferred Qualifications:
  • Master's degree in Computer Science, Engineering, or a related technical field.
  • Experience working in a startup or fast-paced environment.
  • Hands-on experience fine-tuning machine learning models, including supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF or RFT).
  • Solid understanding of generative AI, machine learning principles, and enterprise infrastructure.