Machine Learning Jobs in San Francisco, CA

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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.

CNC Lathe Set-up and Operator

Company: RJ Machine Inc.

Location: San Diego, CA

Posted Apr 11, 2025

Visually inspect tools for excessive wear. Select and install tooling as required by the documentation. We have ample opportunity for people to learn how to…

AI/ML Developer - AI Trainer

Company: DataAnnotation

Location: San Diego, CA

Posted Apr 17, 2025

A bachelor’s degree (completed or in progress). Proficiency in at least one of the following programming languages or frameworks: JavaScript, TypeScript, Python…

CNC Machinist - Lathe

Company: JI Machine Company, Inc.

Location: San Diego, CA

Posted Apr 12, 2025

Minimum 4 yrs experience, proficient at programming and setting up machines. Visual Acuity: Near acuity and accommodation are required for reading machine dial…

Data Analyst, Global Partnerships & Content

Company: Meta

Location: Los Angeles, CA

Posted Apr 17, 2025

Understand partner performance, collaborating with key stakeholders to develop recommendations for measurement. 5+ years of experience in data analytics roles.

Automatic Door Installer & Technician

Company: WALTERS & WOLF INTERIORS

Location: Fremont, CA

Posted Apr 18, 2025

Must have AADM certification and/or working experience with automatic doors, storefront doors, and electrified hardware. Basic power tools will be provided.

Project Lead Operations & Strategy - Kohler Ventures

Company: Kohler

Location: Palo Alto, CA

Posted Apr 15, 2025

Analyze, evaluate, and overcome project risks to enable project completion on time and within budget. Provide support to the broader operations team as needed,…

Medical Case Manager- CA

Company: Broadspire Services, Inc.

Location: San Jose, CA

Posted Apr 17, 2025

Must maintain a valid driver's license in state of residence. A registered nurse (RN) license. This is a work from home position requiring local field case…

Retail Associate, Stanford Shopping Center

Company: The Black Tux

Location: Palo Alto, CA

Posted Apr 15, 2025

You’ll play a key role in ensuring a smooth and enjoyable experience from concept to checkout. If you love styling, problem-solving, and creating memorable…

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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