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Machine Learning Algorithms Jobs (NOW HIRING)

CoVar

Machine Learning Engineer

Durham, NC · On-site

Machine Learning Engineer About CoVar CoVar is a small AI/ML R&D software company in Durham, NC ... You will work with data, develop algorithms, evaluate results, and write the production code that ...

Avride

Machine Learning Engineer

Austin, TX · On-site

Required : • Strong understanding of fundamental machine learning algorithms and neural network techniques. • Expertise in at least one modern machine learning domain, such as computer vision ...

S27a

Machine Learning Engineer

San Francisco, CA · On-site

$150 - $200/hr

Research, design, and implement machine learning algorithms to optimize workflow automation. * Develop, test, and modify computer programs to apply machine learning models to real-world applications.

S27a

$150 - $200/hr

Research, design, and implement machine learning algorithms to optimize workflow automation. * Develop, test, and modify computer programs to apply machine learning models to real-world applications.

Howmet

Machine Learning Engineer

Whitehall, MI · On-site

Build and refine machine learning algorithms to enhance Howmet products across all business units. * Construct, manipulate, and analyze large datasets using tools such as Python and SQL. * Leverage ...

Givzey

Machine Learning Engineer

Design, develop, and implement machine learning models and algorithms * Analyze large datasets and extract meaningful insights * Collaborate with cross-functional teams to integrate ML solutions into ...

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$25.5K

$42.6K

$88K

How much do machine learning algorithms jobs pay per year?

As of Sep 10, 2026, the average yearly pay for machine learning algorithms 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 are machine learning algorithms?

Machine learning algorithms are computational methods that enable computers to learn patterns and make decisions or predictions from data without being explicitly programmed for each task. These algorithms can be classified into categories such as supervised learning, unsupervised learning, and reinforcement learning, each suited for different data and goals. Examples include decision trees, support vector machines, neural networks, and clustering algorithms. The choice of algorithm depends on the type of problem, the nature of the data, and the desired outcome.

What are the key skills and qualifications needed to thrive as a machine learning algorithms engineer?

To excel as a Machine Learning Algorithms Engineer, you need a solid background in mathematics, statistics, programming (especially Python or R), and a relevant degree in computer science or a related field. Familiarity with machine learning frameworks (like TensorFlow, PyTorch, or scikit-learn), data preprocessing tools, and cloud platforms is typically required, along with knowledge of version control systems. Strong analytical thinking, problem-solving abilities, and effective communication skills set top performers apart in this role. These skills and qualities are critical for designing robust models, collaborating with cross-functional teams, and translating complex data into actionable solutions.

What are some common challenges faced when collaborating with cross-functional teams as a machine learning algorithms specialist?

As a Machine Learning Algorithms specialist, collaborating with cross-functional teams such as data engineers, software developers, and product managers can present challenges like aligning on project goals, communicating complex technical concepts to non-experts, and integrating models into existing systems. It's important to establish clear communication channels, define shared objectives early, and actively participate in iterative feedback cycles. These practices help ensure that machine learning solutions are both technically sound and aligned with business needs.

What is the difference between Machine Learning Algorithms vs Data Scientists?

AspectMachine Learning AlgorithmsData Scientists
CredentialsKnowledge of algorithms, programming, statisticsAdvanced degrees in data science, statistics, or related fields
Work EnvironmentDeveloping, testing, and tuning algorithmsAnalyzing data, building models, interpreting results
Industry UsageEmbedded within data science workflows and toolsLeading data analysis projects, decision-making

While machine learning algorithms are the core tools used by data scientists, the role of a data scientist encompasses understanding, applying, and interpreting these algorithms within broader data analysis and business contexts. Machine learning algorithms are technical components, whereas data scientists integrate these tools to derive insights and inform strategies.

What careers are there in machine learning algorithms?

Careers in machine learning algorithms include roles such as machine learning engineer, data scientist, research scientist, and AI developer. These positions typically require skills in programming, statistics, and familiarity with tools like Python, TensorFlow, or PyTorch, and often involve developing models, analyzing data, and deploying AI solutions.
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Machine Learning Algorithms jobs near you
Infographic showing various Machine Learning Algorithms job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 23% Part Time, and 1% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Machine Learning Engineer

Atlanta, GA • On-site

Infinite Resource Solutions
IT Services • 11 - 50 employees
Learn more about Infinite Resource Solutionsexternal

Full-time

Re-posted yesterday

Job description

Job Description Machine Learning Engineer Roles and Responsibilities Lead the end-to-end architecture and development of machine learning solutions. Implement machine learning algorithms into services and pipelines to be consumed at large-scale. Engineer large scale development systems using full-stack, distributed shallow and deep-learning technologies and big data technologies.

Architect and develop a highly scalable, distributed, multi-tenant set of microservices backend solutions. Be a part of a highly productive and creative engineering team What Are We Looking For in This Role. Highly Preferred: MS or PhD in Machine learning, Computer Vision, Natural Language Processing or a related field.

5+ years of experience architecting and developing AI and machine learning applications Ability to think critically, question assumptions and devise solutions to challenging technical problems. Hands-on experience with one or more of the following technologies: --Machine Learning: TensorFlow, PyTorch, Spark ML/MLib etc. --ML Technologies: NLP, Computer Vision and related technologies.

--Back end web-services: Java, Spring Boot, Python, Kubernetes, Docker - Big Data technologies: Kafka, Apache Spark, MapR, Hbase, Hive, HDFS etc. Minimum Qualifications Bachelor's Degree Relevant Experience or Degree in: Computer Science, Management Information Systems, Business or related field Typically Minimum 6 Years Relevant Exp Four-year college degree and 6 or more years, and/or a high school diploma with 8 or more years professional experience with full life cycle design and development

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