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Evening Edge Ai Machine Learning Jobs (NOW HIRING)

Arcfield

Lead Edge AI/ML Engineer

Richmond, VA · On-site +1

$101K - $133K/yr

Lead Edge AI / Machine Learning Engineer Strategic Technology Consulting (STC), an Arcfield Company, is seeking a Lead Edge AI / Machine Learning Engineer to lead the design, optimization, and ...

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 ...

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 ...

Masimo

Engineer II, AI/Machine Learning

Irvine, CA

$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 ...

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All JobsEvening Edge Ai Machine Learning Jobs

Evening Edge Ai Machine Learning information

See salary details

$25.5K

$42.6K

$88K

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

As of Jun 15, 2026, the average yearly pay for evening edge ai machine learning in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

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

AspectEvening Edge Ai Machine LearningData Scientist
Required CredentialsBachelor's degree in CS, Data Science, or related field; experience with ML frameworksBachelor's or Master's in CS, Statistics, or related; often a PhD
Work EnvironmentTech companies, startups, or research labs; project-based workCorporate, consulting, or research institutions; data analysis focus
Industry UsageAI product development, automation, predictive modelingBusiness analytics, strategic insights, data-driven decision making

While both roles involve machine learning skills, Evening Edge Ai Machine Learning focuses on developing AI models and algorithms, often in a technical environment. Data Scientists analyze data to extract insights and inform business decisions. The roles overlap in skills but differ in primary focus and application.

What are Evening Edge AI Machine Learning jobs?

Evening Edge AI Machine Learning jobs typically refer to positions focused on developing, maintaining, or deploying machine learning solutions, with work hours in the evening or during non-traditional schedules. These roles may involve tasks such as data analysis, model training, algorithm development, and performance evaluation, often within teams that require coverage outside of standard office hours. Professionals in these positions may work remotely or on-site, supporting projects that need continuous operation or global collaboration. Evening shifts can be ideal for those seeking flexible work hours or needing to accommodate other commitments.

What are some common challenges faced by Evening Edge AI Machine Learning professionals and how can they be addressed?

Evening Edge AI Machine Learning professionals often encounter challenges such as managing tight project timelines due to after-hours deployments, troubleshooting unexpected model behavior with limited immediate support, and keeping up with rapid advancements in AI technology. To address these issues, it's helpful to develop strong time management skills, proactively communicate with team members during overlapping hours, and regularly participate in knowledge-sharing sessions. Building a network with other AI professionals and leveraging online resources can also help you stay updated and troubleshoot effectively during evening shifts.

What are the key skills and qualifications needed to thrive as an AI Machine Learning Engineer, and why are they important?

To thrive as an AI Machine Learning Engineer, you need a strong background in mathematics, programming (Python, R, or Java), and experience with machine learning algorithms, usually supported by a degree in computer science or a related field. Familiarity with technical tools like TensorFlow, PyTorch, scikit-learn, and cloud platforms such as AWS or Azure, as well as relevant certifications, is highly beneficial. Creative problem-solving, analytical thinking, and effective communication help you translate complex data insights into actionable solutions and collaborate across teams. These skills and qualifications are essential for developing robust AI solutions that drive innovation and meet business objectives.
More about Evening Edge Ai Machine Learning jobs
What cities are hiring for Evening Edge Ai Machine Learning jobs? Cities with the most Evening 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 Evening Edge Ai Machine Learning jobs? States with the most job openings for Evening Edge Ai Machine Learning jobs include:
What job categories do people searching Evening Edge Ai Machine Learning jobs look for? The top searched job categories for Evening Edge Ai Machine Learning jobs are:
Evening Edge Ai Machine Learning jobs near you
Infographic showing various Evening Edge Ai Machine Learning job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Applied Machine Learning Engineer

Fireworks AI

New York, NY

Other

Posted 28 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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