Machine Learning Jobs in Remote

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Looking for Machine Learning jobs in Remote? Browse our curated listings with transparent salary information to find the perfect Machine Learning position in the Remote area.

Program Director

Company: Bounteous

Location: Remote

Posted Feb 03, 2025

Bounteous, a leading digital transformation consultancy, is seeking a Program Director with extensive project management experience, particularly in Agile methodologies. The role involves leading diverse teams, managing project scope, budget, and timeline, and ensuring client satisfaction. The ideal candidate will have a strong understanding of digital development processes, technologies, and data privacy standards. They will also serve as a Scrum Master, facilitating Agile ceremonies, and managing project risks. The candidate should have excellent communication skills, the ability to resolve conflicts, and experience working in regulated environments. Bounteous values diversity and inclusion, and offers opportunities for continuous learning and career growth.

IT Automation Specialist

Company: Kyndryl

Location: Lima, Peru / Remote

Posted Feb 03, 2025

Senior Software Engineer - AI Code Gen

Company: Datadog

Location: Boston, MA / Remote

Posted Feb 03, 2025

DevOps Engineer II

Company: Ansys

Location: Milan, Italy / Remote

Posted Feb 03, 2025

Monitoring & Automation Specialist

Company: Kyndryl

Location: Lima, Peru / Remote

Posted Feb 03, 2025

Accounting and Technology Solutions Advisor

Company: Nava

Location: Remote

Posted Feb 03, 2025

Nava is a consultancy and public benefit corporation that has been working with federal, state, and local government agencies since 2013. They aim to make government services simple, effective, and accessible to all, with a focus on populations that are the least protected. The Accounting Technology and Solutions Advisor role involves bridging the gap between state government accounting practices and technology solutions, ensuring seamless integration and improved efficiency. The ideal candidate should have a minimum of 5 years of accounting experience, strong understanding of financial measurement tools and accounting principles, and experience with budgeting, financial reporting, and compliance with state government regulations.

Frequently Asked Questions

What are typical salary ranges for ML roles at different seniority levels?
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.
What skills and certifications are required for ML positions?
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.
Are ML jobs available for remote work?
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.
What career progression paths exist in ML?
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.
What are current industry trends shaping ML careers?
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.

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