Machine Learning Jobs

Positions 425,970 Updated daily

Machine learning is reshaping industries from finance to healthcare. Demand for talent spikes as companies adopt predictive analytics, automated underwriting, and personalized medicine. Data‑driven product teams need engineers who can translate theory into scalable solutions using TensorFlow, PyTorch, or AWS SageMaker.

Within the field you’ll find roles such as ML Engineer, responsible for productionizing models, Data Scientist building prototypes, Research Scientist pushing algorithmic frontiers, ML Ops Engineer focusing on CI/CD pipelines, and AI Product Manager bridging tech and business. Typical duties include feature engineering, model training, hyper‑parameter tuning, model monitoring, and collaborating with data engineers to maintain data pipelines.

Salary transparency is vital for ML professionals because the field’s rapid evolution creates pay disparities across domains. Knowing the market range for a TensorFlow‑based model deployer in New York versus a research scientist in Seattle helps candidates negotiate realistic offers, assess equity and bonus structures, and prevent skill‑based wage gaps.

Senior Data Scientist (5+ years of experiences )

Company: IBM

Location: Giza, EG

Posted Jan 07, 2026

IBM Consulting seeks Data Scientists with ML and AI expertise to drive innovation. The role emphasizes collaboration, leadership, and technical skills in a global environment.

Associate Business Transformation Consultant - HR Reinvention

Company: IBM

Location: London, GB

Posted Jan 07, 2026

IBM Consulting promotes career growth through collaborative client relationships, innovation in hybrid cloud and AI, and a culture focused on empathy and long-term development. The role involves HR transformation projects with opportunities to implement technology solutions and deliver impactful results.

Associate Experience Consultant-Customer & Experience Transformation

Company: IBM

Location: Multiple Cities

Posted Jan 07, 2026

IBM Consulting's Associate Program emphasizes professional growth, global collaboration, and innovation, offering opportunities to solve complex problems and drive business impact through technology and mentorship.

Expert Labs Delivery Consultant - Early Professional

Company: IBM

Location: Istanbul, TR

Posted Jan 07, 2026

This role offers opportunities to collaborate with clients, solve complex challenges using agile methods, and drive impactful solutions within a supportive team environment.

Application Developer-Cloud FullStack

Company: IBM

Location: BANGALORE, IN

Posted Jan 07, 2026

This job description highlights a career in IBM Consulting focused on hybrid cloud and AI innovation, emphasizing collaboration with global clients, technical expertise, and impactful problem-solving through strategic partnerships and technology platforms.

Network Support Specialist

Company: IBM

Location: BANGALORE, IN

Posted Jan 07, 2026

The text describes IBM's Infrastructure & Technology division, highlighting their role in designing resilient systems and delivering innovative solutions. It outlines a Network Support Engineer position with responsibilities in technical support, presales, and lab activities, along with required certifications and expertise in networking technologies.

Data Scientist (Public sector)

Company: IBM

Location: Multiple Cities

Posted Jan 07, 2026

The text promotes IBM CIC's career opportunities, emphasizing career growth, training programs, innovation, and a supportive work environment. It highlights benefits like flexible working, diversity initiatives, and advanced technology platforms while outlining role responsibilities and required skills for data science positions.

Software Engineer (WordPress Developer)

Company: RebelCode

Location: Anywhere in the World

Posted Feb 09, 2026

Senior penetration tester

Company: IBM

Location: BANGALORE, IN

Posted Jan 07, 2026

IBM emphasizes innovation, security, and professional growth in its job offerings. The role involves penetration testing, threat modeling, and secure coding practices. The company promotes a collaborative environment with opportunities for career development and impactful problem-solving.

Frequently Asked Questions

What are typical salary ranges by seniority for machine learning roles?
Entry‑level ML Engineer: $90k–$120k; Mid‑level ML Engineer or Data Scientist: $120k–$160k; Senior ML Engineer or Research Scientist: $160k–$220k; Lead ML Engineer or Principal Research Scientist: $200k–$280k; AI Product Manager: $130k–$180k depending on experience and market.
What skills and certifications are most valuable in machine learning today?
Core language: Python; Deep learning frameworks: TensorFlow, PyTorch; Scikit‑learn for classical models; SQL and NoSQL databases for data ingestion; Docker and Kubernetes for deployment; Cloud AI services such as AWS SageMaker, GCP Vertex AI, Azure ML. Certifications: TensorFlow Developer Certificate, AWS Certified Machine Learning – Specialty, GCP Professional Machine Learning Engineer.
How common is remote work for machine learning positions?
Over 70% of ML roles allow full remote or hybrid arrangements. Startups and fintech firms tend to offer 100% remote options, while larger enterprises often provide hybrid models with occasional on‑site data‑center visits. Remote work is especially prevalent for roles focused on model training and research.
What career progression paths exist in machine learning?
Typical paths: ML Engineer → Senior ML Engineer → Lead ML Engineer → ML Manager; Data Scientist → Senior Data Scientist → Lead Data Scientist → Head of Data; Research Scientist → Senior Research Scientist → Principal Scientist → Chief Data Scientist; ML Ops Engineer → Senior ML Ops Engineer → Lead ML Ops Engineer → Director of MLOps. Progression often involves moving from coding to architecture, then to leadership and strategy.
What are the current industry trends shaping machine learning hiring?
Key trends: reinforcement learning for autonomous systems; federated learning for privacy‑preserving models; edge AI for IoT devices; AutoML platforms speeding model deployment; MLOps practices for scalable pipelines; explainable AI and ethics compliance; and increased demand for AI governance roles.

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