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Ibm Machine Learning Jobs (NOW HIRING)

DEKA

Machine Learning Engineer

Manchester, NH

We're looking for a Machine Learning Engineer who can operate at the intersection of backend ... Experience with relational and NoSQL databases (Oracle, IBM Db2, MSSQL, MongoDB) * Familiarity with ...

DEKA

Machine Learning Engineer

Manchester, NH · On-site

We're looking for a Machine Learning Engineer who can operate at the intersection of backend ... Experience with relational and NoSQL databases (Oracle, IBM Db2, MSSQL, MongoDB) * Familiarity with ...

Fusemachines

Lead Data Scientist- IBM Watson

New York, NY · On-site

$100K - $150K/yr

Design and implement statistical models, machine learning algorithms, predictive analytics models ... IBM WatsonX: 2+ years of hands-on experience with IBM WatsonX * Agentic AI Tools: Experience with ...

Fusemachines

Lead Data Scientist- IBM Watson

New York, NY · On-site

$100K - $150K/yr

Design and implement statistical models, machine learning algorithms, predictive analytics models ... IBM WatsonX: 2+ years of hands-on experience with IBM WatsonX * Agentic AI Tools: Experience with ...

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$25.5K

$42.6K

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How much do ibm machine learning jobs pay per year?

As of Jun 3, 2026, the average yearly pay for ibm 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 are the key skills and qualifications needed to thrive as an IBM Machine Learning Engineer, and why are they important?

To excel as an IBM Machine Learning Engineer, you need a solid background in computer science, mathematics, and statistics, typically supported by a relevant degree and experience with machine learning algorithms. Familiarity with IBM tools such as Watson Studio, SPSS Modeler, and cloud-based platforms, along with proficiency in Python or R, is essential. Strong problem-solving, communication, and collaboration skills help you translate business requirements into data-driven solutions. These competencies are crucial for effectively designing, deploying, and maintaining impactful machine learning models within IBM's ecosystem.

What types of projects and collaboration can I expect as an IBM Machine Learning specialist?

As an IBM Machine Learning specialist, you can expect to work on diverse projects ranging from developing predictive models to automating business processes with AI. You will frequently collaborate with data scientists, software engineers, and business analysts to translate complex data into actionable insights. The environment is typically agile and encourages cross-functional teamwork, where you'll participate in regular meetings, code reviews, and brainstorming sessions. These collaborations not only enhance project outcomes but also offer valuable opportunities for professional growth and knowledge sharing.

What is an IBM Machine Learning Engineer?

An IBM Machine Learning Engineer is a professional who designs, builds, and deploys machine learning models using IBM's suite of tools and platforms, such as IBM Watson, SPSS Modeler, and IBM Cloud. They work with large datasets to develop predictive models, automate decision-making processes, and solve complex business problems. These engineers collaborate with data scientists, software developers, and business stakeholders to implement AI solutions that drive innovation and efficiency within organizations.

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

AspectIbm Machine LearningData Scientist
Required CredentialsCertifications in IBM AI/ML tools, programming skillsDegree in CS, statistics, or related field; often certifications in data analysis
Work EnvironmentFocus on developing and deploying ML models using IBM platformsData analysis, model building, and interpretation across various tools
Industry UsagePrimarily in organizations using IBM cloud and AI solutionsAcross industries, using diverse tools and programming languages

IBM Machine Learning specialists focus on deploying ML models within IBM ecosystems, while Data Scientists analyze data and build models using various tools. Both roles require programming skills, but Data Scientists often have broader analytical responsibilities. The choice depends on whether you prefer working within IBM platforms or a more general data analysis environment.

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Ibm Machine Learning jobs near you
Infographic showing various Ibm Machine Learning job openings in the United States as of May 2026, with employment types broken down into 2% As Needed, 38% Full Time, and 60% Part Time. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.
Machine Learning Engineer with Security Clearance

Machine Learning Engineer with Security Clearance

Mount Indie, LLC

Reston, VA

Other

Posted 16 days ago

Job description

Mount Indie is looking for a Machine Learning (ML) Engineer with documented expertise to be responsible for researching, developing, architecting, and integrating ML models, algorithms, tools, and techniques into existing or new environments. A candidate who has experience analyzing large datasets with preprocessing data skills, data cleansing, and conducting data integrity and validation actions. A candidate who will design, develop, and integrate ML models and algorithms to address specific problems with detection of AI-generated and manipulated imagery, or introduction of pattern recognition for imagery.

Successful candidates for this role must have critical thinking skills, be creative, curious, resourceful, and have a passion for conveying a wide range of information through research leading to deeper insights. The candidate may work independently but participate in project-wide reviews of requirements, system architecture, and detailed design documents. An ML Engineer must be able to collaborate well with a strong lean-forward attitude to shift knowledge left, deliver well, and produce quality results.

The selected candidate will play a critical role in bridging research and operations, integrating advanced algorithms into operational workflows. * Integrate and operationalize machine learning and computer vision models developed by research partners * Apply algorithms to datasets and generate results aligned with project requirements * Evaluate model performance using metrics such as accuracy, precision, recall, ROC/AUC, and localization quality * Adapt and optimize models for real-world conditions (compression, noise, format variability) * Work with containerized solutions (Docker or similar) to support deployment and reproducibility * Translate research concepts, theory, and technical reports into practical implementations and workflows * Generate technical reports, visualizations, and summaries suitable for stakeholders * Collaborate with internal teams and external partners to support integration and testing * Participate in technical meetings and reviews within secure environments. Candidates must have a complete understanding and wide application of technical principles, theories and concepts.

Working under only general direction, provides technical solutions to a wide range of difficult problems. Independently determines and develops approach to solutions. Required Skills: Clearance Level: TS/SCI US Citizenship: Required * Years of Experience: 5-7 Years relevant data science experience * Education Level: Bachelor degree or Master's degree is required in Operations Research, Industrial Engineering, Applied Mathematics, Statistics, Physics, Computer Science, or related fields.

* 3+ years of experience in machine learning / computer vision / data science * Strong proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow) * Experience working with image and/or video data * Ability to understand and implement research-level algorithms from technical papers and reports * Experience with model evaluation, validation, and performance analysis * Familiarity with Linux-based environments and version control systems (e.g., Git) Desired Skills: * Experience with image forensics, alteration detection, or related analytics * Experience working in classified environments * Familiarity with containerization (Docker) and deployment pipelines * Experience handling large-scale or multi-format datasets (JPG, WEBP, MP4, AVI) * Knowledge of synthetic data generation or explainable AI * Ability to bridge theory and implementation * Strong problem-solving and analytical skills * Effective communication with both technical and non-technical stakeholders * Comfortable working in structured, mission-driven environments Relevant Certifications: * Certifications in machine learning, data science, or related fields (e.g., TensorFlow Developer Certificate, AWS Certified Machine Learning Specialty, IBM Machine Learning Professional Certificate, Google Professional Machine Learning Engineer Certification, IABAC: Certified Machine Learning Expert Certification, etc.

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