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.

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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