{"id":253,"date":"2026-08-01T02:38:38","date_gmt":"2026-08-01T02:38:38","guid":{"rendered":"https:\/\/cybotrix.com\/careers\/?p=253"},"modified":"2026-08-01T12:24:31","modified_gmt":"2026-08-01T12:24:31","slug":"machine-learning-engineer-jobs-in-san-francisco-entry-to-senior-level","status":"publish","type":"post","link":"https:\/\/cybotrix.com\/careers\/artificial-intelligence\/machine-learning-engineer-jobs-in-san-francisco-entry-to-senior-level","title":{"rendered":"Machine Learning Engineer Jobs in San Francisco | Entry to Senior Level AI Careers"},"content":{"rendered":"\n

Build the Future with Machine Learning Careers in San Francisco<\/h2>\n\n\n\n

San Francisco continues to be one of the world’s leading technology hubs, offering exceptional opportunities for Machine Learning Engineers<\/strong> across startups, unicorns, Fortune 500 companies, AI research labs, healthcare organizations, autonomous vehicle companies, cybersecurity firms, and fintech enterprises. Organizations are actively hiring professionals who can build intelligent systems capable of learning from large-scale data and delivering measurable business outcomes.<\/p>\n\n\n\n\n\n\n

Whether you are an entry-level Machine Learning Engineer<\/strong>, a mid-level AI developer<\/strong>, or a Senior Machine Learning Architect<\/strong>, organizations in San Francisco are investing heavily in AI-powered automation, predictive analytics, recommendation engines, natural language processing, computer vision, generative AI, and deep learning technologies.<\/p>\n\n\n\n

If you have expertise in Python, TensorFlow, PyTorch, Scikit-learn, MLOps, AWS, Azure, Google Cloud, and data engineering<\/strong>, this is an excellent opportunity to accelerate your career while working on cutting-edge AI products used by millions worldwide.<\/p>\n\n\n\n

\n\n\n\n

Job Title<\/h1>\n\n\n\n

Machine Learning Engineer<\/strong><\/p>\n\n\n\n

Location<\/h2>\n\n\n\n

San Francisco, California, USA<\/p>\n\n\n\n

Experience<\/h2>\n\n\n\n

Entry Level to Senior Professionals (0\u201312+ Years)<\/p>\n\n\n\n

Employment Type<\/h2>\n\n\n\n
    \n
  • Full-Time<\/li>\n\n\n\n
  • Permanent<\/li>\n\n\n\n
  • Contract<\/li>\n\n\n\n
  • Hybrid<\/li>\n\n\n\n
  • Remote Opportunities Available<\/li>\n<\/ul>\n\n\n\n
    \n\n\n\n

    Salary Range<\/h1>\n\n\n\n
    Experience<\/th>Expected Annual Salary (USD)<\/th><\/tr><\/thead>
    Entry Level<\/td>$110,000 \u2013 $145,000<\/td><\/tr>
    2\u20135 Years<\/td>$145,000 \u2013 $190,000<\/td><\/tr>
    5\u20138 Years<\/td>$190,000 \u2013 $245,000<\/td><\/tr>
    Senior Level<\/td>$245,000 \u2013 $350,000+<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

    Compensation may also include annual bonuses, RSUs, stock options, relocation support, performance incentives, and comprehensive employee benefits.<\/em><\/p>\n\n\n\n\n\n\n

    \n\n\n\n

    About the Opportunity<\/h1>\n\n\n\n

    Artificial Intelligence has become the foundation of modern digital transformation. Organizations across every industry are leveraging Machine Learning Engineers<\/strong> to develop scalable AI platforms that improve operational efficiency, automate business processes, personalize customer experiences, optimize supply chains, detect fraud, and generate predictive insights.<\/p>\n\n\n\n

    As a Machine Learning Engineer, you will collaborate with software engineers, data scientists, product managers, cloud architects, and business stakeholders to design, build, deploy, and maintain production-ready machine learning solutions. You will work with large datasets, cloud-native architectures, distributed computing platforms, and modern MLOps pipelines to deliver high-performance AI applications.<\/p>\n\n\n\n

    This role offers exposure to cutting-edge technologies, including Generative AI, Large Language Models (LLMs), reinforcement learning, vector databases, Retrieval-Augmented Generation (RAG), multimodal AI, explainable AI, and AI governance frameworks<\/strong>.<\/p>\n\n\n\n

    \n\n\n\n

    Key Responsibilities<\/h1>\n\n\n\n
      \n
    • Design scalable machine learning models for production environments.<\/li>\n\n\n\n
    • Build predictive analytics solutions using structured and unstructured datasets.<\/li>\n\n\n\n
    • Develop supervised and unsupervised learning models.<\/li>\n\n\n\n
    • Design feature engineering pipelines for large-scale data processing.<\/li>\n\n\n\n
    • Train, validate, evaluate, and optimize ML models.<\/li>\n\n\n\n
    • Implement deep learning architectures for business applications.<\/li>\n\n\n\n
    • Deploy machine learning models using cloud-native technologies.<\/li>\n\n\n\n
    • Create automated MLOps pipelines for continuous integration and deployment.<\/li>\n\n\n\n
    • Improve model accuracy through experimentation and hyperparameter tuning.<\/li>\n\n\n\n
    • Build recommendation engines and intelligent search solutions.<\/li>\n\n\n\n
    • Develop NLP solutions using transformer-based architectures.<\/li>\n\n\n\n
    • Build computer vision applications where applicable.<\/li>\n\n\n\n
    • Work with distributed data processing frameworks.<\/li>\n\n\n\n
    • Collaborate with DevOps teams for scalable deployment.<\/li>\n\n\n\n
    • Monitor production model performance.<\/li>\n\n\n\n
    • Detect and reduce model drift.<\/li>\n\n\n\n
    • Build APIs for AI model consumption.<\/li>\n\n\n\n
    • Improve inference latency and scalability.<\/li>\n\n\n\n
    • Optimize GPU utilization.<\/li>\n\n\n\n
    • Create reusable machine learning components.<\/li>\n\n\n\n
    • Build explainable AI dashboards.<\/li>\n\n\n\n
    • Maintain documentation for ML workflows.<\/li>\n\n\n\n
    • Participate in architecture discussions.<\/li>\n\n\n\n
    • Review code and mentor junior engineers.<\/li>\n\n\n\n
    • Follow AI security and governance best practices.<\/li>\n<\/ul>\n\n\n\n
      \n\n\n\n\n\n\n

      Required Technical Skills<\/h1>\n\n\n\n
      Category<\/th>Skills<\/th><\/tr><\/thead>
      Programming<\/td>Python, Java, Scala, SQL<\/td><\/tr>
      Machine Learning<\/td>Supervised Learning, Unsupervised Learning, Reinforcement Learning<\/td><\/tr>
      Deep Learning<\/td>TensorFlow, PyTorch, Keras<\/td><\/tr>
      Libraries<\/td>Scikit-learn, XGBoost, LightGBM, Hugging Face<\/td><\/tr>
      NLP<\/td>BERT, GPT, Transformers, Tokenization<\/td><\/tr>
      Computer Vision<\/td>OpenCV, CNN, Vision Transformers<\/td><\/tr>
      Data Processing<\/td>Pandas, NumPy, Spark<\/td><\/tr>
      Cloud Platforms<\/td>AWS, Azure, Google Cloud Platform<\/td><\/tr>
      Containers<\/td>Docker, Kubernetes<\/td><\/tr>
      MLOps<\/td>MLflow, Kubeflow, SageMaker, Vertex AI<\/td><\/tr>
      Version Control<\/td>Git, GitHub, GitLab<\/td><\/tr>
      Databases<\/td>PostgreSQL, MySQL, MongoDB<\/td><\/tr>
      Big Data<\/td>Hadoop, Spark, Kafka<\/td><\/tr>
      APIs<\/td>REST API, FastAPI<\/td><\/tr>
      CI\/CD<\/td>Jenkins, GitHub Actions<\/td><\/tr>
      Monitoring<\/td>Prometheus, Grafana<\/td><\/tr>
      Deployment<\/td>Kubernetes, Docker Swarm<\/td><\/tr>
      Data Pipelines<\/td>Airflow, Prefect<\/td><\/tr>
      Vector Databases<\/td>Pinecone, Milvus, Weaviate<\/td><\/tr>
      LLM Frameworks<\/td>LangChain, LlamaIndex<\/td><\/tr>
      AI Infrastructure<\/td>CUDA, GPU Computing<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
      \n\n\n\n

      Preferred Qualifications<\/h1>\n\n\n\n
        \n
      • Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Mathematics, Statistics, or related field.<\/li>\n\n\n\n
      • PhD candidates with AI research experience are encouraged to apply.<\/li>\n\n\n\n
      • Strong understanding of machine learning algorithms.<\/li>\n\n\n\n
      • Experience deploying enterprise-grade AI systems.<\/li>\n\n\n\n
      • Experience working with cloud infrastructure.<\/li>\n\n\n\n
      • Knowledge of distributed computing.<\/li>\n\n\n\n
      • Familiarity with software engineering best practices.<\/li>\n\n\n\n
      • Strong analytical and problem-solving skills.<\/li>\n\n\n\n
      • Excellent communication and collaboration abilities.<\/li>\n<\/ul>\n\n\n\n
        \n\n\n\n\n\n\n

        Core Responsibilities Throughout the ML Lifecycle<\/h1>\n\n\n\n

        Data Collection<\/h3>\n\n\n\n
          \n
        • Gather structured and unstructured datasets<\/li>\n\n\n\n
        • Build ingestion pipelines<\/li>\n\n\n\n
        • Validate incoming data<\/li>\n<\/ul>\n\n\n\n

          Data Engineering<\/h3>\n\n\n\n
            \n
          • Clean datasets<\/li>\n\n\n\n
          • Remove anomalies<\/li>\n\n\n\n
          • Feature extraction<\/li>\n\n\n\n
          • Feature selection<\/li>\n\n\n\n
          • Feature scaling<\/li>\n<\/ul>\n\n\n\n

            Model Development<\/h3>\n\n\n\n
              \n
            • Select algorithms<\/li>\n\n\n\n
            • Train models<\/li>\n\n\n\n
            • Hyperparameter tuning<\/li>\n\n\n\n
            • Cross validation<\/li>\n\n\n\n
            • Performance optimization<\/li>\n<\/ul>\n\n\n\n

              Model Deployment<\/h3>\n\n\n\n
                \n
              • Build inference APIs<\/li>\n\n\n\n
              • Containerize models<\/li>\n\n\n\n
              • Cloud deployment<\/li>\n\n\n\n
              • CI\/CD automation<\/li>\n<\/ul>\n\n\n\n

                Monitoring<\/h3>\n\n\n\n
                  \n
                • Model drift detection<\/li>\n\n\n\n
                • Performance monitoring<\/li>\n\n\n\n
                • Retraining pipelines<\/li>\n\n\n\n
                • Cost optimization<\/li>\n<\/ul>\n\n\n\n
                  \n\n\n\n

                  Technologies You May Work With<\/h1>\n\n\n\n
                    \n
                  • Python<\/li>\n\n\n\n
                  • TensorFlow<\/li>\n\n\n\n
                  • PyTorch<\/li>\n\n\n\n
                  • Hugging Face<\/li>\n\n\n\n
                  • LangChain<\/li>\n\n\n\n
                  • LlamaIndex<\/li>\n\n\n\n
                  • MLflow<\/li>\n\n\n\n
                  • Kubeflow<\/li>\n\n\n\n
                  • Docker<\/li>\n\n\n\n
                  • Kubernetes<\/li>\n\n\n\n
                  • Apache Spark<\/li>\n\n\n\n
                  • Kafka<\/li>\n\n\n\n
                  • Airflow<\/li>\n\n\n\n
                  • FastAPI<\/li>\n\n\n\n
                  • AWS SageMaker<\/li>\n\n\n\n
                  • Azure Machine Learning<\/li>\n\n\n\n
                  • Google Vertex AI<\/li>\n\n\n\n
                  • Redis<\/li>\n\n\n\n
                  • Elasticsearch<\/li>\n\n\n\n
                  • Pinecone<\/li>\n\n\n\n
                  • Weaviate<\/li>\n\n\n\n
                  • Milvus<\/li>\n\n\n\n
                  • PostgreSQL<\/li>\n\n\n\n
                  • MongoDB<\/li>\n\n\n\n
                  • Snowflake<\/li>\n\n\n\n
                  • Databricks<\/li>\n<\/ul>\n\n\n\n
                    \n\n\n\n\n\n\n

                    Industries Hiring Machine Learning Engineers<\/h1>\n\n\n\n
                      \n
                    • Artificial Intelligence<\/li>\n\n\n\n
                    • Financial Services<\/li>\n\n\n\n
                    • Healthcare<\/li>\n\n\n\n
                    • Biotechnology<\/li>\n\n\n\n
                    • Cybersecurity<\/li>\n\n\n\n
                    • Retail<\/li>\n\n\n\n
                    • E-commerce<\/li>\n\n\n\n
                    • Autonomous Vehicles<\/li>\n\n\n\n
                    • Robotics<\/li>\n\n\n\n
                    • Cloud Computing<\/li>\n\n\n\n
                    • SaaS<\/li>\n\n\n\n
                    • Telecommunications<\/li>\n\n\n\n
                    • Manufacturing<\/li>\n\n\n\n
                    • Logistics<\/li>\n\n\n\n
                    • Insurance<\/li>\n\n\n\n
                    • Media & Entertainment<\/li>\n<\/ul>\n\n\n\n
                      \n\n\n\n

                      Career Progression<\/h1>\n\n\n\n
                        \n
                      • Associate Machine Learning Engineer<\/li>\n\n\n\n
                      • Machine Learning Engineer<\/li>\n\n\n\n
                      • Senior Machine Learning Engineer<\/li>\n\n\n\n
                      • Lead AI Engineer<\/li>\n\n\n\n
                      • Principal Machine Learning Engineer<\/li>\n\n\n\n
                      • AI Solutions Architect<\/li>\n\n\n\n
                      • ML Platform Engineer<\/li>\n\n\n\n
                      • AI Technical Lead<\/li>\n\n\n\n
                      • Engineering Manager<\/li>\n\n\n\n
                      • Director of Artificial Intelligence<\/li>\n\n\n\n
                      • Head of Machine Learning<\/li>\n\n\n\n
                      • Chief AI Officer<\/li>\n<\/ul>\n\n\n\n
                        \n\n\n\n

                        Why Choose San Francisco?<\/h1>\n\n\n\n

                        San Francisco remains one of the strongest global destinations for AI professionals, offering access to leading technology companies, venture-backed startups, research institutions, and innovation ecosystems. Engineers benefit from challenging projects, competitive compensation, exposure to large-scale AI systems, and opportunities to contribute to next-generation technologies in generative AI, autonomous systems, robotics, fintech, healthcare AI, cybersecurity, and enterprise automation<\/strong>.<\/p>\n\n\n\n

                        \n\n\n\n

                        Employee Benefits<\/h1>\n\n\n\n
                          \n
                        • Competitive salary packages<\/li>\n\n\n\n
                        • Annual performance bonuses<\/li>\n\n\n\n
                        • Stock options and RSUs<\/li>\n\n\n\n
                        • Medical, dental, and vision insurance<\/li>\n\n\n\n
                        • 401(k) retirement plans<\/li>\n\n\n\n
                        • Paid holidays and vacation<\/li>\n\n\n\n
                        • Flexible hybrid and remote work<\/li>\n\n\n\n
                        • Learning and certification reimbursement<\/li>\n\n\n\n
                        • Conference sponsorship<\/li>\n\n\n\n
                        • Wellness programs<\/li>\n\n\n\n
                        • Parental leave<\/li>\n\n\n\n
                        • Relocation assistance<\/li>\n\n\n\n
                        • Career development plans<\/li>\n\n\n\n
                        • Internal AI innovation programs<\/li>\n<\/ul>\n\n\n\n
                          \n\n\n\n\n\n\n

                          Why Apply?<\/h1>\n\n\n\n

                          This opportunity allows you to work on real-world AI products that solve complex business problems at scale. You will collaborate with highly skilled engineers, data scientists, and researchers while building production-grade machine learning systems used by global customers. Whether your focus is predictive analytics, computer vision, NLP, recommendation systems, or Generative AI, this role offers significant career growth, technical challenges, and long-term advancement.<\/p>\n\n\n\n

                          \n

                          Machine Learning Engineer Jobs in San Francisco, AI Engineer Jobs, Deep Learning Engineer, Machine Learning Careers, Python Machine Learning Jobs, TensorFlow Jobs, PyTorch Jobs, MLOps Engineer, NLP Engineer Jobs, Computer Vision Engineer, Artificial Intelligence Jobs, Generative AI Jobs, LLM Engineer Jobs, Data Science Jobs San Francisco, AI Careers USA<\/p>\n<\/blockquote>\n","protected":false},"excerpt":{"rendered":"

                          Build the Future with Machine Learning Careers in San Francisco San Francisco continues to be one of the world’s leading technology hubs, offering exceptional opportunities for Machine Learning Engineers across startups, unicorns, Fortune 500 companies, AI research labs, healthcare organizations, autonomous vehicle companies, cybersecurity firms, and fintech enterprises. Organizations are actively hiring professionals who can build intelligent systems capable of learning from large-scale data and delivering measurable business outcomes. Whether you are an entry-level Machine Learning Engineer, a mid-level AI developer, or a Senior Machine Learning Architect, organizations in San Francisco are investing heavily in AI-powered automation, predictive analytics, recommendation engines, natural language processing, computer vision, generative AI, and deep learning technologies. If you have expertise in Python, TensorFlow, PyTorch, Scikit-learn, MLOps, AWS, Azure, Google Cloud, and data engineering, this is an excellent opportunity to accelerate your career while working on cutting-edge AI products used by millions worldwide. Job Title Machine Learning Engineer Location San Francisco, California, USA Experience Entry Level to Senior Professionals (0\u201312+ Years) Employment Type Salary Range Experience Expected Annual Salary (USD) Entry Level $110,000 \u2013 $145,000 2\u20135 Years $145,000 \u2013 $190,000 5\u20138 Years $190,000 \u2013 $245,000 Senior Level $245,000 \u2013 $350,000+ Compensation may also include annual bonuses, RSUs, stock options, relocation support, performance incentives, and comprehensive employee benefits. About the Opportunity Artificial Intelligence has become the foundation of modern digital transformation. Organizations across every industry are leveraging Machine Learning Engineers to develop scalable AI platforms that improve operational efficiency, automate business processes, personalize customer experiences, optimize supply chains, detect fraud, and generate predictive insights. As a Machine Learning Engineer, you will collaborate with software engineers, data scientists, product managers, cloud architects, and business stakeholders to design, build, deploy, and maintain production-ready machine learning solutions. You will work with large datasets, cloud-native architectures, distributed computing platforms, and modern MLOps pipelines to deliver high-performance AI applications. This role offers exposure to cutting-edge technologies, including Generative AI, Large Language Models (LLMs), reinforcement learning, vector databases, Retrieval-Augmented Generation (RAG), multimodal AI, explainable AI, and AI governance frameworks. Key Responsibilities Required Technical Skills Category Skills Programming Python, Java, Scala, SQL Machine Learning Supervised Learning, Unsupervised Learning, Reinforcement Learning Deep Learning TensorFlow, PyTorch, Keras Libraries Scikit-learn, XGBoost, LightGBM, Hugging Face NLP BERT, GPT, Transformers, Tokenization Computer Vision OpenCV, CNN, Vision Transformers Data Processing Pandas, NumPy, Spark Cloud Platforms AWS, Azure, Google Cloud Platform Containers Docker, Kubernetes MLOps MLflow, Kubeflow, SageMaker, Vertex AI Version Control Git, GitHub, GitLab Databases PostgreSQL, MySQL, MongoDB Big Data Hadoop, Spark, Kafka APIs REST API, FastAPI CI\/CD Jenkins, GitHub Actions Monitoring Prometheus, Grafana Deployment Kubernetes, Docker Swarm Data Pipelines Airflow, Prefect Vector Databases Pinecone, Milvus, Weaviate LLM Frameworks LangChain, LlamaIndex AI Infrastructure CUDA, GPU Computing Preferred Qualifications Core Responsibilities Throughout the ML Lifecycle Data Collection Data Engineering Model Development Model Deployment Monitoring Technologies You May Work With Industries Hiring Machine Learning Engineers Career Progression Why Choose San Francisco? San Francisco remains one of the strongest global destinations for AI professionals, offering access to leading technology companies, venture-backed startups, research institutions, and innovation ecosystems. Engineers benefit from challenging projects, competitive compensation, exposure to large-scale AI systems, and opportunities to contribute to next-generation technologies in generative AI, autonomous systems, robotics, fintech, healthcare AI, cybersecurity, and enterprise automation. Employee Benefits Why Apply? This opportunity allows you to work on real-world AI products that solve complex business problems at scale. You will collaborate with highly skilled engineers, data scientists, and researchers while building production-grade machine learning systems used by global customers. Whether your focus is predictive analytics, computer vision, NLP, recommendation systems, or Generative AI, this role offers significant career growth, technical challenges, and long-term advancement. Machine Learning Engineer Jobs in San Francisco, AI Engineer Jobs, Deep Learning Engineer, Machine Learning Careers, Python Machine Learning Jobs, TensorFlow Jobs, PyTorch Jobs, MLOps Engineer, NLP Engineer Jobs, Computer Vision Engineer, Artificial Intelligence Jobs, Generative AI Jobs, LLM Engineer Jobs, Data Science Jobs San Francisco, AI Careers USA<\/p>\n","protected":false},"author":1,"featured_media":162,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[35],"tags":[419,378,416,418,179,422,362,381,354,180,197,382,355,176,417,365,234,377,181,363,356,364,353,420,376,375,383,415,214,371,385,113,358,386,421,467,360,145,357,465,370,374],"class_list":["post-253","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","tag-airflow","tag-apache-spark","tag-aws-sagemaker","tag-azure-machine-learning","tag-ci-cd","tag-cloud-computing","tag-computer-vision","tag-data-engineering","tag-deep-learning","tag-docker","tag-fastapi","tag-feature-engineering","tag-generative-ai","tag-git","tag-google-vertex-ai","tag-hugging-face","tag-kafka","tag-kubeflow","tag-kubernetes","tag-langchain","tag-large-language-models-llms","tag-llamaindex","tag-machine-learning","tag-milvus","tag-mlflow","tag-mlops","tag-model-deployment","tag-nlp","tag-nosql","tag-pinecone","tag-predictive-analytics","tag-python","tag-pytorch","tag-reinforcement-learning","tag-rest-apis","tag-san-francisco","tag-scikit-learn","tag-sql","tag-tensorflow","tag-usa","tag-vector-databases","tag-weaviate"],"yoast_head":"\nMachine Learning Engineer Jobs in San Francisco | Entry to Senior Level AI Careers - Cybotrix<\/title>\n<meta name=\"description\" content=\"Explore Machine Learning Engineer Jobs in San Francisco for entry to senior professionals. 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