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Machine Learning Engineer Jobs in Columbia, SC (NOW HIRING)

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Staff Forward Deployed Engineer

North, SC · On-site +1

$100K - $500K/yr

Applied Engineer, Machine Learning Engineer, MLOps Engineer, Platform Engineer, Infrastructure Engineer, Site Reliability Engineer, Field Application Engineer). * Experience turning ambiguous ...

AI Engineer

Columbia, SC · On-site

$50K - $112K/yr

Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or related data and AI credentials - Building ...

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

We're looking for engineers fluent in modern AI application development - agent frameworks, harnesses, tool use, prompting, retrieval, and evaluation - not machine learning engineers. You will be ...

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Post Doctoral

Columbia, SC · On-site

$45K - $61K/yr

D.) degree in Chemical Engineering, Materials Science, Computational Chemistry, Chemical Physics, or a closely related field. Experience with molecular simulation, machine learning, or related ...

Software Developer (Columbia, SC) Design, develop, and maintain backend software systems and ... Integrate data enrichment processes and machine learning models into data processing workflows to ...

Post Doctoral

Columbia, SC · On-site

$45K - $61K/yr

D.) degree in Chemical Engineering, Materials Science, Computational Chemistry, Chemical Physics, or a closely related field. • Experience with molecular simulation, machine learning, or related ...

Demonstrated proficiency in AI and ML technologies, including modeling, and machine learning ... Hands-on experience with DevOps practices, including CI/CD pipelines, automated testing ...

... analytics engineering focus on leveraging advanced technologies and techniques to design and ... Those in artificial intelligence and machine learning at PwC will focus on developing and ...

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Machine Learning Engineer information

See Columbia, SC salary details

$29.1K

$119.1K

$179K

How much do machine learning engineer jobs pay per year?

As of Aug 18, 2026, the average yearly pay for machine learning engineer in Columbia, SC is $119,129.00, according to ZipRecruiter salary data. Most workers in this role earn between $93,900.00 and $143,400.00 per year, depending on experience, location, and employer.

What is a machine learning engineer?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What does a machine learning engineer do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are the key skills and qualifications needed to thrive as a machine learning engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What are some common challenges faced by machine learning engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

What are the most commonly searched types of Machine Learning Engineer jobs in Columbia, SC?

The most popular types of Machine Learning Engineer jobs in Columbia, SC are:

What job categories do people searching Machine Learning Engineer jobs in Columbia, SC look for?

The top searched job categories for Machine Learning Engineer jobs in Columbia, SC are:

What cities near Columbia, SC are hiring for Machine Learning Engineer jobs?

Cities near Columbia, SC with the most Machine Learning Engineer job openings:

Infographic showing various Machine Learning Engineer job openings in Columbia, SC as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 22% Part Time, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $119,129 per year, or $57.3 per hour.

Senior AI/ML C++ software engineer

Recruiting Engine (MLS)

Lexington, SC

$104K - $138K/yr

Full-time

Re-posted 27 days ago


Job description

Senior Embedded Controls Engineer: C++/Linux and Machine Learning exp.
As an AI Machine Learning Engineer focus will be on designing and developing scalable solutions using AI tools and machine learning models. Addressing various neural network-related challenges in transportation sector. This involves leveraging big data computation and storage tools to create prototypes and datasets, conducting model training and evaluations, integrating solutions, performing bench tests and onsite tests, tuning, and monitoring. Proficiency in languages such as C and C++ is required, along with software development for Linux platforms.
Your responsibilities
Design and develop real time AI ? Neural Network solutions for transportation industry maintenance equipment. Implementing appropriate ML algorithms.
Write clean, documented code following best practices.
Develop and implement communication protocols.
Work independently and collaboratively with a motivated team.
Generate requirements and design documentation.
Plan for, design, and deliver testing, and tested products into the QA process.
Apply communication and problem-solving skills to solve software issues related to the design, development, deployment, testing, and operation of systems.
Qualifications
Education
Master"s / Bachelor"s degree in Software Engineering or similar experience.
Experience
5+ years of experience in developing CNN, R-CNN type neural network for computer vision tasks.
5+ years of experience in Software development using C++ & Linux embedded.
Experience with Supervised and Semi-Supervised Learning, Deep Learning, Support Vector Machines, Linear and Logistic Regression.
Working knowledge of AI Framework such as TensorFlow, Caf?, PyTorch, Keras, Darknet and OpenCV.
Working knowledge of AI edge devices such as NVIDIA Jetson / Nano / Orin.
Knowledge of the Linux Operating System.
Preferred Experience
Experience using statistical computer languages (R, Python, SQL etc.) to manipulate data and draw insights from large data sets.
Experience working with and creating data architectures.
Knowledge of a variety of machine learning techniques (semantic segmentation, clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, and proper usage, etc.) and experience with applications.
Experience with edge computing & controlling devices (On-device deployment in C/C++ or similar) for real time application.
Experience with optimizing neural networks to perform well on low-power mobile platforms (e.g. pruning, distillation, quantization).