Machine Learning Jobs in Remote

Junior ML Engineers earn $90k–$120k annually, mid‑level engineers $120k–$160k, senior engineers $160k–$220k, and lead or principal ML roles can reach $220k–$300k+. In large tech firms, the upper end can exceed $350k when including equity, while early‑stage startups may offer lower base but higher stock options.

Core expertise includes Python, Jupyter, TensorFlow, PyTorch, scikit‑learn, and SQL. MLOps proficiency with Docker, Kubernetes, and cloud services (AWS SageMaker, GCP AI Platform, Azure ML) is essential for production roles. Certifications such as TensorFlow Developer, AWS Certified Machine Learning – Specialty, and Google Cloud Professional Machine Learning Engineer can validate knowledge and accelerate hiring.

Yes, many ML positions are fully remote or hybrid. Companies like Scale AI, Databricks, and Cohere offer remote‑first policies. Remote work requires high‑speed internet, secure VPN access, and collaboration via tools like JupyterHub, Slack, and Asana, but it also expands the geographic talent pool.

Typical paths start as ML Engineer or Data Scientist, advance to Senior ML Engineer, Lead Data Scientist, or Research Scientist, then transition into managerial roles such as ML Manager, Director of AI, or VP of Data & AI. Progression hinges on building a strong portfolio, publishing research, mentoring junior teammates, and mastering cross‑functional skills like product strategy and ethics.

Edge AI and federated learning are driving demand for on‑device models; AutoML platforms reduce time to deployment; responsible AI frameworks (e.g., IBM AI Fairness 360) shape compliance roles; reinforcement learning is expanding into robotics; and interpretability tools like SHAP and LIME are becoming standard in regulated sectors such as finance and healthcare.