Machine Learning Jobs in San Francisco, CA

295,205 open positions · Updated daily

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

Senior Staff Engineer, Product Software

Company: Equinix, Inc

Location: Redwood City, CA / Remote

Posted Feb 06, 2025

View Details Apply on The Muse

Deburr Technician, Machining (Starship) - 2nd Shift

Company: SpaceX

Location: El Segundo, CA

Posted Feb 06, 2025

View Details Apply on The Muse

Residential Estate Security Agent- Armed

Company: Black Box Security

Location: Los Angeles, CA

Posted Feb 06, 2025

The Job:* Provide concierge level Residential Estate Protection to high-profile and high-net-worth clients and their families while maintaining the utmost level…

View Details Apply on SimplyHired

Lead Technician I

Company: Restoration Management Company

Location: Anaheim, CA

Posted Feb 06, 2025

View Details Apply on The Muse

Territory Sales Representative

Company:

Location: Palm Springs, CA

Posted Feb 06, 2025

View Details Apply on The Muse

Teacher at Grant Elementary

Company: KinderCare Learning Companies

Location: Petaluma, CA

Posted Feb 06, 2025

View Details Apply on The Muse

Solution Consultant, Revenue Cycle

Company:

Location: San Diego, CA

Posted Feb 06, 2025

View Details Apply on The Muse

Data Scientist III, Research, Brand Advertising, YouTube Ads

Company: Google

Location: Mountain View, CA

Posted Feb 06, 2025

View Details Apply on The Muse

Sales Representative

Company:

Location: Palm Springs, CA

Posted Feb 06, 2025

View Details Apply on The Muse

Sr. Computer Vision & Machine Learning Engineer, Creativity Apps

Company: Apple

Location: Cupertino, CA

Posted Feb 06, 2025

View Details Apply on The Muse

Principal Logistics Planner

Company: Northrop Grumman

Location: Rosamond, CA

Posted Feb 06, 2025

View Details Apply on The Muse

Enterprise Infrastructure Engineer

Company: Meta

Location: Fremont, CA

Posted Feb 06, 2025

View Details Apply on The Muse

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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