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

Develop and deploy applied AI and machine learning systems for customer-facing agents. Responsibilities include model fine-tuning, reinforcement learning, evaluation design, large-scale text and audio data work, post-training, voice AI, long-horizon optimization, and production deployment. The role translates customer and product challenges into ML problems and carries solutions from experimentation through measurable customer impact.

General interest posting for candidates who attended COLM 2026 and connected with Scale AI. The company is seeking potential candidates passionate about machine learning, AI research, generative AI, defense applications, and autonomous vehicles for future opportunities across multiple roles.

Own the machine-learning signals and decisions governing web crawling and indexing at massive scale. Develop classifiers, rankers, and calibrated models to assess page quality, credibility, freshness, parsing success, duplicate content, and indexing value. Define ground truth for ambiguous problems, validate measurable search improvements, and collaborate across crawling, indexing, and retrieval teams to improve the quality and coverage of search results.

Build data preprocessing and generation pipelines, run model training and fine-tuning workflows, design benchmarking and evaluation suites, and operationalize research models into reproducible production workflows. The role collaborates with researchers, communicates metrics and results, and owns measurable machine learning outcomes from data preparation through delivery.

Develop mathematical models for music preferences, similarity, engagement, personalization, and discovery. Build and deploy scalable real-time recommendation and ranking systems, translate product objectives into metrics, run large-scale experiments and causal analyses, and collaborate with product and research leaders on personalization strategy.

Develop deep learning models using imitation and reinforcement learning to generate and evaluate autonomous vehicle trajectories. Build production machine learning pipelines, metrics, and error-analysis tools, leveraging large-scale infrastructure. Collaborate with Planning, Perception, Simulation, and Systems Engineering teams to improve autonomous driving behavior in complex urban environments.

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 and deploy scalable AI/ML systems for retrieval, ranking, categorization, triage, and generative AI across healthcare data. Own solutions end-to-end from problem framing and model selection through production deployment and optimization. Collaborate with software engineers, product managers, clinicians, and operations teams to translate complex business problems into explainable, maintainable systems with measurable outcomes.

Design and deploy production machine learning models and conversational AI agents that transform advertiser briefs into optimized media plans. Build evaluation systems to improve model and agent quality, collaborate across science and engineering teams, investigate ambiguous problems, and iterate using performance data and seller feedback. Communicate effectively with technical and non-technical stakeholders while contributing broadly across machine learning domains.

Lead the design, development, testing, deployment, and productization of scalable AI/ML solutions. Build and optimize machine learning and deep learning models, transform prototypes into secure enterprise systems, and develop production-grade MLOps pipelines and CI/CD workflows. Guide AI/ML strategy, roadmaps, cloud deployments, and enterprise integration while mentoring engineering teams, advising customers, supporting workforce planning, and collaborating with technical and government stakeholders.

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.

Architect and build terabyte-scale robotics data pipelines from fleet sensor streams through training ingestion. Curate, preprocess, annotate, version, and validate datasets; analyze data coverage, drift, redundancy, and quality; mine scenarios using embeddings and models; and develop samplers, benchmarks, and evaluation datasets. Partner with ML researchers to diagnose model failures and create targeted data mixtures and processing workflows that improve robot-learning performance.

Build and optimize end-to-end machine learning training infrastructure for large-scale multimodal robot data. Responsibilities include distributed training, data ingestion and transformation, checkpointing, orchestration, experiment tracking, sampling and curation, performance profiling, debugging data and training issues, and converting research prototypes into production infrastructure. The role may also involve robot learning, cloud and cluster infrastructure, post-training, on-robot inference optimization, CUDA or Triton kernels, and video-heavy datasets.

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.

Build scalable data pipelines and production AI systems for networking and security telemetry. Develop ETL/ELT workflows in Python and SQL, create and monitor LLM, RAG, and machine learning applications, and support anomaly detection, troubleshooting, and incident analysis. Collaborate with engineers and researchers on maintainable code, dashboards, design reviews, and live production services.

Build and operate a scalable model API platform for data ingestion, fine-tuning, evaluation, low-latency inference, and deployment. Design reliable multi-tenant infrastructure with rate limiting, isolation, observability, versioning, and SLOs. Collaborate with researchers and external partners to turn evolving AI capabilities into intuitive developer tools, APIs, and documentation. The role emphasizes production software engineering, backend systems, infrastructure, and model serving rather than robotics research or customer success.

Build and own a scalable API platform that enables external organizations to upload data, fine-tune and evaluate AI models, and run low-latency inference for robotics applications. Responsibilities include designing multi-tenant infrastructure, APIs, deployment integrations, observability, reliability, and developer tooling. The role requires strong backend and systems engineering skills, Python expertise, production machine learning familiarity, and end-to-end ownership of ambiguous platform problems.

Design, train, evaluate, and deploy machine learning models that assess complex technical work across images, geometry, and interaction logs. Translate expert judgment into training signals, labels, datasets, and evaluation criteria. Manage the full learning loop, analyze model failures, collaborate with domain specialists, and deliver production models for live decision-making using Python and PyTorch.

Build production recommendation, scoring, optimization, and reinforcement-learning systems that adjust advertising spend, budgets, and campaign status. Design feedback loops and multi-agent reasoning systems using live results and shared context. Own decision quality and financial outcomes while applying statistics, A/B testing, and advertising optimization concepts such as pacing, budget allocation, ROAS, and CPA targets.

Build and deploy production multimodal machine learning systems for images and video, including model orchestration, evaluation, fine-tuning, edge inference, runtime optimization, and interactive audio-visual interfaces. The role involves deploying open-weight models on constrained or on-premise hardware, creating agent workflows and feedback loops, and moving prototypes into dependable field use for hardware-based products.

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.

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.

Build and own Clera’s AI/ML intelligence layer, including recommendation systems, ranking algorithms, and LLM-powered candidate-role matching. Develop production-ready features, data pipelines, and services while collaborating cross-functionally and shipping frequently. The role requires hands-on experience with the listed development tools, TypeScript, React, startup environments, and a willingness to work six days per week in a San Francisco hacker house.