Machine Learning Jobs

425,594 open positions · Updated daily

Machine learning is reshaping industries from autonomous vehicles to personalized medicine. Every quarter, investment in AI startups rises, and Fortune 500s are hiring data scientists to turn vast datasets into actionable insights. This surge translates into a high demand for ML talent, with roles expanding into new verticals and requiring hybrid skill sets that blend software engineering, domain knowledge, and statistical modeling.

Within the ML ecosystem, you’ll find positions such as ML Engineer, responsible for production‑ready pipelines; Data Scientist, focused on exploratory analysis and model prototyping; Research Scientist, pushing the frontier of deep learning; MLOps Engineer, ensuring seamless deployment and monitoring; and AI Ethicist, guiding responsible AI practices. Each role demands tools like TensorFlow, PyTorch, scikit‑learn, SQL, Docker, Kubernetes, and cloud ML services (AWS SageMaker, GCP AI Platform).

Salary transparency matters because it levels the playing field for ML professionals. With clear pay data, you can benchmark your compensation against peers, negotiate offers that reflect your expertise, and spot disparities that may indicate bias. Transparent figures also help you assess the true cost of entry into specialized subfields like reinforcement learning or federated learning, where pay can differ significantly from standard ML engineering.

Senior Staff Machine Learning Engineer - Feed Relevance

Company: Reddit

Location: USA

Posted Nov 08, 2025

The job posting is for a Senior Staff Machine Learning Engineer to join the Feed Relevance team at Reddit. The role is completely remote-friendly and involves delivering technical initiatives, setting technical direction, mentoring engineers, and creating a strong engineering culture. The position offers comprehensive healthcare benefits, 401k matching, workspace benefits, personal and professional development funds, family planning support, flexible vacation, Reddit Global Wellness Days, 4+ months paid parental leave, and paid volunteer time off.

Senior Android Engineer - Trust Platform

Company: Airbnb

Location: USA

Posted Nov 08, 2025

The Trust team at Airbnb is responsible for protecting the community and platform from fraud while ensuring high standards for hosts, guests, homes, and experiences. The Android Software Engineer role involves developing and evolving trust and safety defenses on the Android app, working closely with cross-platform engineers to define and shape the future of the friction framework, and contributing to core metrics, developer experience, and reliability.

Lead – Data Engineer I

Company: Ollion

Location: Remote

Posted Nov 07, 2025

Ollion emphasizes innovation and independence, offering scalable solutions and a collaborative environment with competitive benefits. The company highlights its global team's ability to drive transformative change while prioritizing customer impact and flexible work arrangements.

Staff Machine Learning Engineer - User Understanding

Company: Pinterest

Location: USA

Posted Nov 08, 2025

The User Understanding team at Pinterest develops advanced models to deeply understand user interests, intents, and tastes. The Staff Machine Learning Engineer will lead the team in building next-generation user understanding models, collaborate cross-functionally, drive experimentation, manage project execution, and provide thought leadership in user modeling and recommender systems.

Business Value Strategist

Company: Saviynt

Location: USA

Posted Nov 08, 2025

Saviynt is a leading identity security platform that helps organizations safeguard digital assets, drive operational efficiency, and reduce compliance costs. The company is seeking a Business Value Strategist to quantify and drive strategic decision-making, assess financial impact, and communicate the value of their solutions to clients.

Housing Senior Support Consultant

Company: NECSWS

Location: Remote

Posted Nov 07, 2025

NEC Software Solutions offers a positive work environment with competitive benefits, flexible hours, and opportunities to make an impact through innovative technology solutions. The job description highlights technical support roles with growth potential, emphasizing collaboration with public sector organizations and career development prospects.

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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