Top Entry Level Machine Learning Jobs in San Francisco Bay Area, CA

Develop and implement frontier-model post-training methods, including reinforcement learning, verifiable rewards, evaluation, and data-centric optimization. Design rigorous experiments across datasets, rewards, environments, and training strategies; investigate model behavior and failure modes; build scalable data-generation and evaluation pipelines; and create benchmarks, rubrics, and scoring systems. Collaborate with researchers, engineers, AI teams, customers, and domain experts while contributing to open-source tools, technical reports, blog posts, and research papers.

Run and evaluate machine learning training experiments for autonomous robots, curate and clean real-world fleet data, improve data-labeling structures, track model performance metrics, and collaborate with Teleoperations to improve data collection. The role focuses on production ML for perception, decision-making, and control, including imitation learning, reinforcement learning, and deployment evaluation on live robotic systems.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Develop machine learning and personalization systems for real-time advertising at planetary scale. Conduct architectural research on attention-based and post-attention models, evaluate hypotheses offline and online, improve recommender and targeting systems, and transition successful research into production. Build model implementations and data pipelines, guide infrastructure investments, collaborate with engineering and product teams, and contribute to the research community through publications, talks, and conferences.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Build next-generation voice AI systems for consumer and business applications. Develop persistent memory, complex reasoning, speech pipelines, and production-grade agentic workflows. As an early technical contributor, the ML Engineer will help shape core architecture, take ambiguous concepts through production, and influence user interactions with advanced language models in a fast-paced pre-seed startup.

Develop and deploy machine learning solutions for construction-site understanding using computer vision, multimodal AI, VLMs, LLMs, generative AI, RAG, and agent architectures. Curate high-quality datasets, optimize models for performance across hardware targets, and build production ML pipelines in the cloud. Own components from design through deployment and monitoring while collaborating in an agile team.

Build and operate machine learning models powering roleplay, scoring, and coaching products. Own the full lifecycle from training and fine-tuning through production deployment and maintenance. Fine-tune open-source models, optimize them for cost and latency, deploy them on-device when needed, and develop evaluation frameworks, benchmarks, and regression suites. Collaborate closely with founders and engineers to shape product direction.

Develop and post-train deep learning models while building systems, tooling, and workflows for training, evaluation, debugging, and deployment. Contribute to open-source projects, distributed AI infrastructure, backend services, and developer platforms. Collaborate with research, product, infrastructure, and customer teams to solve complex technical problems, prototype ideas, and productionize successful experiments.

Own end-to-end machine learning model quality for autonomous robots, including dataset curation, training, evaluation, and production deployment. Work with multimodal sensor data from cameras, IMUs, LiDAR, and odometry to develop perception, planning, navigation, and manipulation models. Build low-latency, continuous inference systems and collaborate with teleoperations and fleet operations teams. Experience with large-scale multimodal models, distributed GPU training, production ML code, and cluster management is valuable.

Build reliable machine learning and data systems that transform assay videos, scientific data, and experimental outcomes into reproducible models, evaluated predictions, and experiment recommendations. Responsibilities include data and feature pipelines, model training and evaluation infrastructure, batch and online inference systems, monitoring, data quality, versioning, observability, and interfaces across computer vision, molecular modeling, active learning, and laboratory workflows.

Build large-scale machine learning systems for advertising, including ranking, recommendation, targeting, prediction, optimization, and creative generation. Develop feedback loops connecting generative AI outputs to advertising performance signals, apply prompt engineering and post-training methods, run experiments, and ship reliable production ML systems. Collaborate across Product, Research, Engineering, and go-to-market teams to deliver AI-native advertising products.

Research Engineer responsible for developing and productionizing post-training methods for diffusion and flow models, including reward modeling, preference optimization, supervised fine-tuning, distillation, and reinforcement learning. The role owns research planning, evaluation standards, training pipelines, and the feedback loop between model behavior and product data. Models will be deployed to professional designers, requiring rigorous engineering, reproducible evaluations, and close collaboration with product and founders.

Build and improve applied AI models for scientific customer workflows. Responsibilities include model post-training, evaluation loop development, experiment design, debugging model behavior, and integrating AI capabilities into production software. The role collaborates with AI researchers and software engineers to adapt models, create reusable tooling, and translate customer feedback into model improvements.

Develop, train, evaluate, and deploy machine learning models and AI-powered applications. Responsibilities include data preprocessing, feature engineering, deep learning development, cloud-based model deployment, and collaboration with data engineers, MLOps teams, and business stakeholders. The role also requires applying strong analytical and research skills while staying current with advances in artificial intelligence.

This Machine Learning Engineer L2 position is an incomplete job requisition template. It does not provide specific responsibilities, qualifications, technologies, education requirements, or measurable role details. The position is described as remote-first at Twilio, with occasional travel potentially required for team or project meetings.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

The AI/Machine Learning Specialist designs and implements AI solutions, collaborates with cross-functional teams, and continuously improves ML processes.

Four-month, full-time research fellowship focused on ML systems and performance. Fellows build and evaluate infrastructure-heavy projects (e.g., CPU simulators, accelerator backends, large-scale distributed training), use external models/APIs, receive mentorship, funding for compute, and aim to produce public research outputs.

Research and develop multimodal perception and authentication systems using visual, audio, and sensor data. Build specialized and multimodal models, design training and evaluation methods, analyze robustness and failure modes, and integrate capabilities into real-time, resource-constrained systems. Collaborate with hardware, firmware, software, and product teams to transition research into practical products.

Develops and deploys machine learning systems for high-stakes decisions in pricing, claims, underwriting, and predictive health. Responsibilities span problem framing, quantitative research, experimentation, causal inference, predictive modeling, multimodal and foundation model development, agent systems, production deployment, and ongoing iteration. The role requires strong mathematical and statistical foundations, Python fluency, and expertise in machine learning, statistics, econometrics, or causal inference.

Build and deploy low-latency machine learning models and inference infrastructure for Netflix’s advertising platform. Develop identity resolution, behavioral and contextual targeting, lookalike expansion, user signals, and audience features at massive scale. Collaborate with science, product, engineering, operations, design, and research teams to productionize models while supporting privacy, governance, security, and advertiser brand safety.

Lead end-to-end development of an AI-driven decision-support system for multi-sensor data. Responsibilities include building data ingestion and fusion pipelines, training and deploying supervised and unsupervised ML models, optimizing near-real-time inference, creating GPU-accelerated dashboards, integrating modular containerized systems, and supporting secure deployment and testing. The role works directly with the founder, owns technical delivery autonomously, and collaborates with subcontractors on human factors, model validation, and security auditing.

As an AI/ML Engineer, you will design, train, and deploy ML models for flight data analysis, providing actionable performance feedback for pilots.

As an AI/ML Engineer, you will design and deploy models for real-world applications, optimize data pipelines, and engage with global leaders to enhance AI technology's impact across sectors like healthcare and government.