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Afternoon Mechanical Engineering Machine Learning Jobs

ISEE

Research Scientist - Machine Learning

... Mechanical Engineering, Control Engineering, Machine Learning, Statistics, or a related field - Passionate about self-driving vehicles and real-world robotics solutions. - Experience collecting ...

ISEE

Research Scientist - Machine Learning

... Mechanical Engineering, Control Engineering, Machine Learning, Statistics, or a related field - Passionate about self-driving vehicles and real-world robotics solutions. - Experience collecting ...

TEKsystems

Machine Learning Engineer

Raleigh, NC · On-site

$75 - $85/hr

This role goes beyond traditional machine learning engineering. The ideal candidate will have experience designing intelligent AI systems capable of reasoning, planning, retrieval, tool utilization ...

Merican

Data Science & Machine Learning Engineer

$117K - $140K/yr

Required Skills & Responsibilities: * 5+ years of experience in Data Engineering, Machine Learning Engineering, or related fields. * Strong programming expertise in Python (Pandas, NumPy, PySpark ...

Hadrian

Machine Learning Engineer

Los Angeles, CA · On-site

Required : • Bachelors in Computer Science, Electrical Engineering, Mechanical Engineering (or ... Machine Learning, including ownership of projects throughout the entire ML Lifecycle • ...

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Afternoon Mechanical Engineering Machine Learning information

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$45.5K

$102.9K

$166.5K

How much do afternoon mechanical engineering machine learning jobs pay per year?

As of Sep 15, 2026, the average yearly pay for afternoon mechanical engineering machine learning in the United States is $102,878.00, according to ZipRecruiter salary data. Most workers in this role earn between $81,500.00 and $126,500.00 per year, depending on experience, location, and employer.

What is an afternoon mechanical engineering machine learning job?

An Afternoon Mechanical Engineering Machine Learning job typically refers to a position where professionals apply machine learning techniques to solve problems in mechanical engineering, with working hours scheduled in the afternoon. These roles often involve analyzing engineering data, developing predictive models, and optimizing mechanical systems using advanced algorithms. The work may include tasks such as fault detection, predictive maintenance, or process optimization, leveraging both engineering expertise and machine learning skills. Employees in such positions usually have backgrounds in both mechanical engineering and computer science or data analytics.

What are the key skills and qualifications needed to thrive as an afternoon mechanical engineering machine learning professional?

To excel in this role, you need a solid background in mechanical engineering principles, mathematics, and machine learning concepts, usually supported by a relevant engineering degree. Familiarity with technical tools such as Python, MATLAB, CAD software, and machine learning frameworks (like TensorFlow or scikit-learn) is typically required. Strong analytical thinking, problem-solving, and effective teamwork are valuable soft skills for integrating machine learning with mechanical systems. These competencies are crucial for developing innovative solutions and optimizing engineering processes with data-driven approaches.

How do mechanical engineers specializing in machine learning typically collaborate with other departments during afternoon shifts?

Mechanical engineers working in machine learning often collaborate closely with data scientists, software developers, and production teams, especially during afternoon shifts when testing and implementation often ramp up. They may participate in cross-functional meetings to align on project goals, troubleshoot issues with live data, and refine machine learning models based on feedback from manufacturing or operations staff. This collaborative environment helps ensure that algorithms are practical, efficient, and aligned with real-world applications. Effective communication and adaptability are key, as priorities can shift rapidly based on production needs.

What is the difference between Afternoon Mechanical Engineering Machine Learning vs Afternoon Mechanical Engineering Data Analysis?

AspectAfternoon Mechanical Engineering Machine LearningAfternoon Mechanical Engineering Data Analysis
Required CredentialsBachelor's or Master's in Mechanical Engineering, proficiency in machine learning toolsBachelor's or Master's in Mechanical Engineering, strong data analysis skills
Work EnvironmentResearch labs, tech companies, manufacturing firmsDesign firms, manufacturing plants, research institutions
Employer & Industry UsageTech-driven engineering sectors applying AI/MLTraditional engineering sectors focusing on data interpretation
Search & Comparison IntentUnderstanding roles involving AI/ML in mechanical engineeringComparing data analysis tasks within mechanical engineering

Afternoon Mechanical Engineering Machine Learning focuses on applying AI and machine learning techniques to mechanical engineering problems, often requiring programming and data modeling skills. In contrast, Afternoon Mechanical Engineering Data Analysis emphasizes interpreting and visualizing data to inform engineering decisions. Both roles share foundational engineering knowledge but differ in their technical focus and application areas.

Can mechanical engineers work in machine learning?

Mechanical engineers can work in machine learning by applying their knowledge of systems, modeling, and data analysis to develop algorithms and models for automation, robotics, and predictive maintenance. Gaining skills in programming languages like Python, and understanding data science tools, can facilitate their transition into machine learning roles. Interdisciplinary expertise and additional training in machine learning techniques are often required.
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Research Scientist - Machine Learning

Remote

ISEE
Trucking • 51 - 200 employees
Learn more about ISEEexternal

Full-time

Re-posted 13 days ago

Job description

ISEE is seeking full-time Research Scientists to join our team. The ideal candidate has several years of research/work experience.
Role responsibilities include:
- Leading research contributing to the development of humanistic AI for autonomous systems
- Conducting research in the areas of: Robotics, Artificial Intelligence, Machine Learning, Perception, Modeling, Simulation, Applied Mathematics, Probabilistic Modeling and Inference or related areas
- Collaborating with engineering and business teams to develop novel solutions to real-world problems
- Designing experiments and metrics for benchmarking resultsPublishing research in top-tier conferences and journals
Qualifications:
- PhD in Robotics, Computer Science, Electrical Engineering, Aerospace Engineering, Mechanical Engineering, Control Engineering, Machine Learning, Statistics, or a related field
- Passionate about self-driving vehicles and real-world robotics solutions.
- Experience collecting, processing, and analyzing real-world data.
- Strong presentation and communication skills.
Recommended
- Publication record in top-tier conferences in Robotics and related fields.
- Programming experience, ideally with Python and/or C++.
- Robotics experience is a plus.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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