ZipRecruiter

Log In

1

Automotive Machine Learning Jobs (NOW HIRING)

Ascentt

Senior Machine Learning Engineer

Plano, TX · On-site

$100K - $137K/yr

Ascentt is building cutting-edge data analytics & AI/ML solutions for global automotive and manufacturing leaders. We turn enterprise data into real-time decisions using advanced machine learning and ...

PhysicsX

Machine Learning Engineer

New York, NY · On-site

... Automotive. Note: We are currently recruiting for multiple positions, however please only apply for ... Who We're Looking For As a Machine Learning Engineer in Delivery, you are a problem solver who ...

Cox Automotive

Machine Learning Lead Engineer

Austell, GA

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

New

Cox Automotive

Machine Learning Lead Engineer

Smyrna, GA

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

New

Corning

AI/Machine Learning Engineer

Wilmington, NC

$82K - $112K/yr

You can help connect the unconnected, drive the future of automobiles, transform at-home ... Apply data science techniques, such as machine learning, statistical modeling, and artificial ...

Cox Automotive

Machine Learning Lead Engineer

Decatur, GA

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

New

next page

Showing results 1-20

People also search for

All JobsAutomotive Machine Learning Jobs

Automotive Machine Learning information

See salary details

$11

$19

$37

How much do automotive machine learning jobs pay per hour?

As of Jun 7, 2026, the average hourly pay for automotive machine learning in the United States is $19.57, according to ZipRecruiter salary data. Most workers in this role earn between $14.42 and $22.12 per hour, depending on experience, location, and employer.

How does an Automotive Machine Learning specialist typically collaborate with cross-functional teams in the automotive industry?

In the automotive industry, a Machine Learning specialist often works closely with software engineers, data scientists, mechanical engineers, and product managers. This collaboration ensures that machine learning models are effectively integrated into vehicle systems, such as advanced driver-assistance systems (ADAS) or autonomous driving features. Specialists may participate in regular meetings, contribute to code reviews, and help interpret data-driven insights for non-technical stakeholders. Effective communication and teamwork are essential for successfully deploying models that meet both technical and regulatory requirements.

What are the key skills and qualifications needed to thrive in Automotive Machine Learning, and why are they important?

To thrive in Automotive Machine Learning, you need a strong background in computer science, mathematics, and machine learning principles, often supported by a relevant degree and experience in automotive systems. Familiarity with programming languages like Python, machine learning frameworks such as TensorFlow or PyTorch, and automotive-specific tools or simulation environments is typically required. Strong problem-solving abilities, teamwork, and effective communication help professionals collaborate across multidisciplinary teams and translate complex data into actionable insights. These skills are crucial for developing reliable, safe, and innovative machine learning solutions in the rapidly evolving automotive industry.

What is automotive machine learning?

Automotive machine learning refers to the use of artificial intelligence (AI) and machine learning algorithms in the automotive industry. These technologies are applied to improve vehicle safety, enable autonomous driving, optimize manufacturing processes, and enhance user experiences. Machine learning models can analyze data from sensors, cameras, and other vehicle systems to make real-time decisions, such as detecting obstacles, recognizing traffic signs, and predicting maintenance needs. As the automotive industry advances, machine learning is becoming essential for developing smart, connected, and self-driving vehicles.

What is the difference between Automotive Machine Learning vs Automotive Data Analyst?

AspectAutomotive Machine LearningAutomotive Data Analyst
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of ML algorithmsDegree in Data Analytics, Statistics, or related fields; proficiency in data analysis tools
Work EnvironmentDeveloping ML models for vehicle systems, working with engineers and data scientistsAnalyzing vehicle data, generating reports, supporting decision-making
Employer & Industry UsageAutomotive manufacturers, tech companies focusing on autonomous vehiclesAutomotive OEMs, suppliers, dealerships, and service centers

Automotive Machine Learning specialists focus on developing algorithms to improve vehicle systems, while Automotive Data Analysts interpret data to support business decisions. Both roles require strong analytical skills but differ in technical depth and application focus.

Infographic showing various Automotive Machine Learning job openings in the United States as of May 2026, with employment types broken down into 5% As Needed, 79% Full Time, 11% Part Time, and 5% Contract. Highlights an 97% Physical, 1% Hybrid, and 2% Remote job distribution, with an average salary of $40,714 per year, or $19.6 per hour.
Senior Machine Learning Engineer

Senior Machine Learning Engineer

Ascentt

Plano, TX • On-site

$100K - $137K/yr

Full-time

Posted 27 days ago

Job description

Ascentt is building cutting-edge data analytics & AI/ML solutions for global automotive and manufacturing leaders. We turn enterprise data into real-time decisions using advanced machine learning and GenAI. Our team solves hard engineering problems at scale, with real-world industry impact. We're hiring passionate builders to shape the future of industrial intelligence.
About the Role:
We are looking for an experienced Senior Machine Learning Engineer with deep expertise in statistical and machine learning techniques, large-scale data processing, and model deployment in cloud environments. The ideal candidate will be a self-starter with strong problem-solving skills and hands-on experience in building and deploying ML models using big data technologies like PySpark and cloud platforms like Amazon SageMaker.
Key Responsibilities:
  • Design, develop, and deploy scalable machine learning models for real-world business problems using structured and unstructured data.
  • Analyze large datasets using PySpark and other distributed computing frameworks to extract insights and prepare features for ML pipelines.
  • Apply a wide range of statistical, machine learning, and deep learning techniques, including but not limited to regression, classification, clustering, time-series forecasting, and NLP.
  • Own end-to-end ML pipelines from data ingestion, preprocessing, training, validation, tuning, and deployment.
  • Utilize Amazon SageMaker or similar platforms for building, training, and deploying models in a production-grade environment.
  • Collaborate closely with data engineers, data scientists, and product teams to integrate models with business workflows.
  • Monitor and improve model performance, scalability, and reliability in production.
  • Contribute to setting up and maintaining the ML environment and tooling (including environment configuration, CI/CD pipelines for ML, model versioning, etc.).

Required Qualifications:
  • 7+ years of experience in machine learning, data science, or related fields.
  • Strong programming skills in Python with experience in ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
  • Hands-on experience with PySpark for big data processing and model development.
  • Proficient in building models on large-scale datasets (terabytes to petabytes).
  • Solid understanding of statistical analysis, probability, hypothesis testing, and experimental design.
  • Experience with Amazon SageMaker (or similar cloud-based ML platforms).
  • Strong knowledge of ML Ops practices including version control, model monitoring, and retraining strategies.
  • Familiarity with containerization (Docker) and CI/CD practices for ML projects is a plus.
  • Excellent communication skills and the ability to clearly explain complex concepts to non-technical stakeholders.

Preferred Qualifications:
  • Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative discipline.
  • Experience with workflow orchestration tools (e.g., Airflow, Kubeflow).
  • Prior experience in domains like Manufacturing, finance, healthcare, or e-commerce is a plus.

Apply