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.

Form Carpenter

Company: Turner Construction Company

Location: San Antonio, TX

Posted Feb 14, 2025

Proficient knowledge of materials, methods, and tools involved in construction or repair of buildings or other structures, foundations, and framing.

Foundation Repair/Retrofitting Construction Crew Lead - EXPERIENCE REQUIRED

Company: The Foundation Works

Location: Burbank, CA

Posted Feb 17, 2025

*Your own transportation and a valid driver’s license*. If you do not have any experience with foundation repair, but you have general construction experience,…

Maintenance Electrician Level 2 (2nd shift)

Company: Shultz Steel

Location: South Gate, CA

Posted Feb 13, 2025

Person, as defined in those regulations, and able to supply evidence of that qualification prior to starting work or be authorized to receive controlled…

AI/ML Developer - AI Trainer

Company: DataAnnotation

Location: Los Angeles, CA

Posted Feb 21, 2025

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

AI Research and Development Engineer

Company: Faraday Future

Location: Gardena, CA

Posted Feb 20, 2025

Develop novel state-of-the-art neural networks and push the boundaries of AI research and engineering for autonomous driving.

Press Brake Operator / Operator de Freno de Prensa 5 years of experience required.

Company: MacSmith Corporation

Location: Gardena, CA

Posted Feb 15, 2025

Read and interpret *blueprints, work orders, and technical drawings*. Operate *press brake machines* to bend, cut, and shape metal components.

Senior Machine Learning Engineer

Company: Exact Sciences

Location: San Diego, CA

Posted Feb 20, 2025

Ph.D. in Statistics, Computational Biology, Computer Science, or related quantitative field as outlined in the essential duties, or master’s degree in…

Project Manager - Land Development & Infrastructure Design

Company: ESP Associates

Location: San Antonio, TX

Posted Feb 15, 2025

ESP Associates has an immediate opportunity in our San Antonio office for a Project Manager with experience in civil engineering land development,…

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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