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Manager Edge Ai Machine Learning Jobs (NOW HIRING)

Merican

AI / Machine Learning Engineer

PA · On-site

AI / Machine Learning Engineer (Contract) Location: Philadelphia, PA or Charlotte, NC Duration: 6 ... Build, orchestrate, and manage intelligent workflows using LangChain and LangGraph. * Integrate AI ...

Samsara

Staff Machine Learning Engineer - Edge AI

... and product managers, and use metrics to ensure our work is translating into better customer ... Machine Learning Engineer. * Profound experience in optimizing ML models and systems for Edge ...

Masimo

Engineer II, AI/Machine Learning

Irvine, CA · On-site

$120K - $150K/yr

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 ...

Masimo

Engineer II, AI/Machine Learning

Irvine, CA · On-site

$120K - $150K/yr

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 ...

Fireworks AI

Applied Machine Learning Engineer

New York, NY · On-site

$170K - $240K/yr

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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Manager Edge Ai Machine Learning information

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$31K

$77.4K

$130K

How much do manager edge ai machine learning jobs pay per year?

As of Jul 7, 2026, the average yearly pay for manager edge ai machine learning in the United States is $77,379.00, according to ZipRecruiter salary data. Most workers in this role earn between $59,000.00 and $87,500.00 per year, depending on experience, location, and employer.

What is the difference between Manager Edge Ai Machine Learning vs Data Scientist?

AspectManager Edge Ai Machine LearningData Scientist
Required CredentialsBachelor's or Master's in Computer Science, AI, or related fields; certifications in AI/MLBachelor's or Master's in Data Science, Statistics, Computer Science; often certifications in data analysis or ML
Work EnvironmentLeads teams, manages projects, collaborates with stakeholders in AI/ML applicationsAnalyzes data, develops models, and provides insights, often working independently or in teams
Employer & Industry UsageTech companies, AI startups, enterprises implementing AI solutionsResearch institutions, tech firms, finance, healthcare, and other data-driven industries

While both roles involve AI and machine learning, the Manager Edge Ai Machine Learning focuses on leading teams and managing AI projects, whereas Data Scientists primarily analyze data and develop models. The manager role emphasizes leadership and project oversight, while Data Scientists concentrate on technical analysis and model development.

More about Manager Edge Ai Machine Learning jobs
What cities are hiring for Manager Edge Ai Machine Learning jobs? Cities with the most Manager Edge Ai Machine Learning job openings:
What are the most commonly searched types of Edge Ai Machine Learning jobs? The most popular types of Edge Ai Machine Learning jobs are:
What states have the most Manager Edge Ai Machine Learning jobs? States with the most job openings for Manager Edge Ai Machine Learning jobs include:
What job categories do people searching Manager Edge Ai Machine Learning jobs look for? The top searched job categories for Manager Edge Ai Machine Learning jobs are:
Manager Edge Ai Machine Learning jobs near you
Infographic showing various Manager Edge Ai Machine Learning job openings in the United States as of July 2026, with employment types broken down into 75% Full Time, 22% Part Time, and 3% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution, with an average salary of $77,379 per year, or $37.2 per hour.

Applied Machine Learning Engineer

Fireworks AI

New York, NY • On-site

Other

Posted 20 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.

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