Top Machine Learning Jobs in San Francisco Bay Area, CA

The Data Scientist will partner with Sales, Revenue Operations, and Finance to analyze GTM performance, build and productionize machine learning models, conduct experiments, forecast outcomes, and identify growth opportunities. Responsibilities include statistical analysis, causal inference, Python and SQL development, MLOps, agentic workflow automation, semantic-layer contributions, executive reporting, and communicating insights to nontechnical stakeholders in a fully remote environment.

Lead and grow a lean Applied AI engineering team while personally building agentic systems, prototypes, evaluations, and AI-native tools. Partner across Sales, Marketing, Operations, and Engineering to deliver measurable business outcomes, establish AI quality standards, test emerging models, and hand stable solutions to durable owners. The role requires managing engineers while remaining hands-on, connecting technical work to revenue and efficiency metrics, and creating processes for rapid experimentation and delivery.

Lead applied AI research focused on information retrieval, RAG, agentic workflows, ranking, reasoning, and evaluation. Build prototypes, golden datasets, quality metrics, regression detection, and experimentation frameworks; investigate failure modes and validate improvements through rigorous testing. Collaborate with AI and software engineers to productionize successful approaches and stay current with research in RAG, agents, LLM evaluation, and relevance modeling.

Research Engineer responsible for identifying weaknesses in voice AI models, defining improvement targets, designing evaluations, curating and generating data, fine-tuning deep learning models, and rapidly shipping production systems. The role emphasizes end-to-end ownership of customer problems, measurable production improvements, practical judgment, and fast experimentation. Candidates should have strong Python and PyTorch skills, production ML experience, and ideally experience with speech technologies, distributed training, publications, open source, or public benchmarks.

Research and improve voice AI models by identifying production weaknesses, defining evaluation metrics, curating data, fine-tuning deep learning systems, and rapidly shipping solutions. The role focuses on speech-to-text, text-to-speech, voice agents, model evaluation, customer-driven research, and production machine learning systems.

Develop and deploy scalable machine-learning solutions, particularly deep-learning forecasting models for company top-line metrics. Lead the full ML lifecycle from problem definition and experimentation through production deployment and improvement. Design production-grade architectures, evaluate and improve models, collaborate with cross-functional teams, explain technical concepts to varied audiences, and mentor junior engineers.

Develop and deploy machine learning and LLM capabilities for agentic search, query understanding, search planning, and retrieval. Build evaluation datasets, improve model behavior through experimentation and fine-tuning, and optimize quality, latency, cost, and reliability. Collaborate across search, product, infrastructure, and AI teams while owning projects through production and mentoring engineers.

Principal Machine Learning Engineer responsible for setting technical direction and building production-grade AI systems for Confluence. The role spans model evaluation, retrieval, ranking, prompt and workflow design, experimentation, reliability, and customer-facing AI product development. Responsibilities include making architectural decisions, partnering across engineering and product teams, mentoring engineers, identifying model and product failure modes, and delivering scalable AI experiences across content creation, editing, discovery, recommendations, and multimodal interaction.

Build and evaluate applied machine learning and Generative AI solutions for product and business problems. Interns will contribute to models, agents, retrieval, experimentation, evaluation, and prototyping with mentorship from experienced engineers. Responsibilities include translating business needs into technical projects, analyzing errors and user feedback, measuring outcomes, and clearly documenting findings, trade-offs, limitations, and next steps.

Build and maintain core infrastructure for developing, training, evaluating, deploying, and operating machine learning models and pipelines for Atlassian’s GenAI products. Solve complex software infrastructure and architecture challenges, lead engineers through technical projects from design to launch, and collaborate with internal teams and customers. The role involves large-scale distributed systems, data processing, cloud platforms, CI/CD, and machine learning lifecycle support.

Lead the technical direction for Atlassian’s Search Serving platform, building globally scalable, low-latency agentic search systems. Own architecture for retrieval, indexing, query processing, and ML inference while improving quality, reliability, scalability, operability, and cost. Establish production ML practices for evaluation, experimentation, observability, rollouts, and incident response. Optimize inference for embedding, retrieval, and ranking models, and lead cross-team initiatives while mentoring senior engineers and aligning technical leaders.

Lead the design, development, deployment, and optimization of production machine learning systems for search, recommendations, personalization, NLP, deep learning, and AI applications. Own projects across the full ML lifecycle, design scalable architectures and evaluation strategies, collaborate with product and engineering teams, translate research into reliable systems, and mentor other engineers.

Own the architecture and evolution of core Weights & Biases ML workflow systems, including Artifacts, Registry, Automations, and Launch. Design scalable services, optimize complex systems, lead migrations and re-architecture, reduce technical debt, and improve reliability. Work across Go backends, GraphQL APIs, Python SDKs, and TypeScript frontends while collaborating with product, design, customers, and ML platform teams. Mentor engineers and establish engineering best practices for high-volume, production AI infrastructure.

Lead the design, development, deployment, and scaling of machine learning models and AI systems powering Atlassian products. Responsibilities include architecture design, experimentation, evaluation, collaboration with product and engineering teams, technical mentorship, and contributing to company-wide AI strategy. The role focuses on search, agentic AI, language models, and intelligent product capabilities.

Build and deploy agentic and generative AI copilot and autopilot systems for risk and compliance automation. Own end-to-end ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based model development. Collaborate with product, engineering, platform, and operations teams to improve fraud detection and analyst productivity. Drive strategic roadmaps and cross-functional programs with significant business impact.

Build and deploy agentic and generative AI systems that automate risk and compliance investigations. Own full-stack ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based modeling across transaction, graph, text, and profile data. Collaborate with product, engineering, platform, and operations teams while leading strategic roadmaps and delivering scalable production systems.

Develops advanced analytics and machine learning models for client business challenges, particularly using pharmaceutical and healthcare data. Translates complex analyses into actionable business recommendations, communicates findings to clients, manages analytical workstreams, supports project milestones, builds client relationships, and mentors junior team members.

Leads CDW’s AI advisory consulting portfolio, including strategy, governance, operating models, adoption, prioritization, and roadmaps. Develops consulting methodologies, delivery frameworks, sales enablement, and thought leadership. Advises executives and customers, leads complex presales pursuits, shapes proposals and business cases, oversees delivery quality and risk, evaluates technical feasibility, and mentors consulting and presales teams.

Build and deploy machine learning models for identity, fraud, and credit risk; research solutions, develop scalable modeling frameworks, integrate LLMs, monitor model performance, and collaborate with Engineering, Product, Design, and Risk stakeholders. The role also requires communicating findings to nontechnical audiences and identifying opportunities to use data to improve products and risk management.

Partner with Slack’s GTM organization to use advanced analytics, experimentation, statistics, and predictive modeling to improve growth, customer outcomes, sales performance, and strategic decision-making. Build scalable analytical frameworks, forecasting methods, measurement strategies, and intelligence solutions. Collaborate with Data Engineering, Product, Sales Strategy, Finance, and executives to develop trusted datasets, communicate insights, and influence GTM strategy.

Lead strategic customers and partners in designing, implementing, and optimizing scalable cloud architectures for AI/ML and GPU workloads. Serve as a trusted technical advisor, conduct workshops and proofs of concept, modernize workloads, deploy and fine-tune LLMs, and accelerate cloud adoption. Collaborate with sales, engineering, product, and operations teams to solve complex challenges, provide product feedback, and support account expansion, retention, and long-term technical success.

Build simulated software environments, evaluation harnesses, grading systems, benchmarks, labeled datasets, and test suites for AI systems. Review interaction logs to identify failure patterns, create reusable evaluation materials, and ensure metrics reflect real-world performance without being gamed. Collaborate with founders on data and training agreements while owning technical problems from diagnosis through delivery.

Lead the AI x Lab Integration research agenda, developing models that understand scientific protocols, plan and execute experiments, diagnose failures, and adapt based on results. Recruit and mentor research scientists and engineers, establish rigorous evaluations, collaborate with experimental scientists and laboratory teams, and guide research in post-training, reinforcement learning, tool use, and learning from execution feedback.

Build and own AI-powered procurement systems, including distributed services, data pipelines, and cloud infrastructure. Explore and apply cutting-edge AI and LLM technologies while solving complex technical challenges in a fast-paced startup. The role requires end-to-end ownership, adaptability, collaboration, and mentorship, with occasional extended workweeks expected.

Own the customer-to-data production feedback loop for audio AI models. Run experiments, ablations, and benchmarks; analyze model behavior; improve data collection, filtering, weighting, annotation, and QA; and build evaluation tooling and pipelines. Translate research findings and customer requirements into rapid product and production changes while collaborating with research engineers and technical stakeholders. Help shape experimentation practices, engineering culture, and the team roadmap.