Top Machine Learning Jobs in San Francisco Bay Area, CA

Develop and scale machine learning algorithms, models, infrastructure, and data pipelines supporting AI features across Atlassian products. Responsibilities include designing model and system architectures, conducting experimentation and evaluations, collaborating with product, engineering, and analytics teams, building production-quality solutions using large datasets, and mentoring junior ML engineers.

Design, develop, deploy, and optimize foundational AI systems, including LLM inference, foundation model training, agentic workflows, vector search, guardrails, evaluation, and observability. Lead multi-model orchestration, cost-performance governance, architecture reviews, technical roadmaps, and responsible AI standards. Mentor senior technical staff and collaborate with engineers, scientists, product managers, and program managers to deliver scalable AI products.

Build and ship production backend services and AI-powered voice agent features. Own customer problems end to end, develop reusable agent modules, improve menu and catalog accuracy, contribute to model fine-tuning, run experiments, and integrate solutions into production. The role involves close customer collaboration, rapid experimentation, and working across Kotlin, Python, and TypeScript in an in-person San Francisco environment.

Set organization-wide AI architecture direction, establish evaluation and production-readiness practices, guide teams from prototypes to production, and build reusable patterns for reliable, secure, observable AI systems. Partner with product and engineering leaders to connect AI initiatives to measurable workflow outcomes. Prototype high-risk designs, review implementations, evaluate emerging models and agent frameworks, and advise on trade-offs among quality, autonomy, latency, cost, safety, and complexity.

Develops core AI capabilities for Atlassian’s DevAI products, including LLM-based methodologies, prompt engineering, end-to-end AI features, data preprocessing, feature engineering, experimentation, model evaluation, pipeline optimization, and monitoring. Collaborates with data scientists, software engineers, and cross-functional partner teams to solve complex problems and apply advances in machine learning and AI.

Build and improve Confluence AI experiences across frontend, backend, and AI integrations. Own features from design through launch, apply generative AI, retrieval, agents, and evaluation techniques, create automated quality testing, monitor production performance, run adoption experiments, and collaborate cross-functionally. Mentor engineers and contribute to architecture, observability, testing, safeguards, and scalable software delivery.

Develops end-to-end AI features for Atlassian’s developer AI products, including LLM integration, prompt engineering, machine learning algorithms, data preprocessing, feature engineering, experimentation, evaluation, pipeline optimization, and monitoring. Collaborates with data scientists, software engineers, and partner teams to solve complex problems and apply advances in AI and machine learning.

Analyze large datasets using quantitative methods, data mining, statistical modeling, and AI tools to generate actionable product and business insights. The role designs and tracks product metrics, builds dashboards and reports, identifies trends and opportunities, and supports data-driven decisions. Responsibilities include collaborating with product managers, engineers, marketers, and designers, applying statistical techniques such as A/B testing and causal analysis, and independently driving projects to completion.

Lead the design and delivery of scalable machine learning systems and customer-facing generative AI applications for Rovo Chat. Build agent harnesses that improve quality, reliability, and latency; advance state-of-the-art AI capabilities; lead engineers through technical design and launch; and collaborate cross-functionally with teams and internal customers. The role also involves applying a deep understanding of the machine learning project lifecycle and supporting platform-based product development.

Leads strategy, roadmap, investment, and execution for horizontal AI capabilities, shared AI platforms, and AI-native cybersecurity products. Translates AI research into reliable, secure, scalable customer solutions; drives adoption across product teams; establishes launch and autonomy criteria; partners with customers and cross-functional leaders on commercialization; and builds a high-performing AI product organization.

Lead the vision, strategy, and roadmap for AI experiences across Atlassian’s core products. Partner with engineering, design, data science, platform, and product teams to deliver intuitive AI features, accelerate adoption, define success metrics, gather customer feedback, and iterate based on data. Serve as an AI product leader, mentor stakeholders, communicate with executives, and drive innovation across a global software portfolio.

Leads the Safety AI organization and architecture for on-device and cloud-based computer vision and machine learning systems. Oversees sensor fusion, model lifecycle management, platform architecture, experimentation, deployment, monitoring, and continuous improvement across more than two million dashcams. Partners with product, firmware, full-stack, and platform teams to deliver reliable, low-latency safety features. Builds and develops teams of applied scientists and engineers while driving measurable improvements in driver safety and customer outcomes.

Lead the design and implementation of AI initiatives for edge devices. Develop and optimize machine learning solutions using large-scale sensor, video, text, diagnostic, transaction, and location data. Partner with engineering and business teams to prototype AI experiences, ship production systems, adopt research advances, and improve edge model performance. Mentor ML engineers and communicate technical concepts to varied audiences.

Leads a multidisciplinary data science and data engineering team supporting Finance. Sets strategy, prioritizes initiatives, guides analytical models and data architecture, advances AI and machine learning applications, improves data quality and reliability, establishes scalable operating processes, and communicates technical recommendations to Finance and executive stakeholders. The role also manages talent, oversees forecasting and decision-support outcomes, and drives measurable business impact.

Leads GitLab’s Product, Sales, and Finance Data Science teams, managing managers and a 12-person organization. Sets data science standards, prioritizes investments, partners with executives, drives product adoption and revenue growth, and oversees experimentation, causal inference, predictive modeling, and AI initiatives. Develops talent, builds succession, and collaborates with Analytics Engineering and Data Engineering on a shared data and AI roadmap.

Build and lead production machine learning and evaluation systems for Atlassian’s Rovo AI agents. Responsibilities include developing datasets, simulations, automated judges, experimentation platforms, and quality signals; advancing reasoning, planning, retrieval, memory, and tool use; identifying and correcting systemic failures; and balancing quality, safety, latency, reliability, and cost. The role requires hands-on technical leadership, cross-functional influence, architectural decision-making, mentorship, and delivery of measurable customer outcomes at scale.

Analyze acquisition data and guide marketing strategy, budget allocation, channel optimization, and growth decisions. Design incrementality experiments, develop marketing mix and allocation models, define acquisition metrics, identify new growth opportunities, and improve web conversion. Partner with Marketing, Engineering, Finance, Design, and User Research to translate statistical and machine learning insights into measurable business outcomes across paid, organic, consumer, and enterprise channels.

Build AI agent orchestration, routing, planning, memory, and action systems. Develop and deploy machine learning models for search ranking and proactive suggestions, integrate modern LLMs, and design evaluations to improve quality, speed, and cost. The role requires independent ownership, strong product sense, and collaboration in a fast-paced environment.

Conducts research evaluating AI-generated digital twins against human responses. Designs experiments, statistical measures, human rating studies, longitudinal data collection, and model evaluations. Analyzes large-scale behavioral data, assesses model calibration and simulation performance, specifies analyses for engineers, and co-authors academic or industry publications.

Leads Snap’s Content Machine Learning Engineering organization, setting multi-year strategy for recommendation, ranking, content understanding, reliability, and experimentation. Oversees distributed teams, develops organizational roadmaps, partners with executives and cross-functional leaders, influences scalable architecture decisions, and builds high-performing engineering teams. The role requires strong expertise in machine learning, deep learning, AI, ranking systems, and large-scale engineering leadership.

Leads AI transformation, governance, quality oversight, risk management, and responsible adoption across Pfizer Global Supply and QSEO. Establishes GxP-compliant AI lifecycle practices, inspection-readiness frameworks, governance forums, portfolio monitoring, inventory management, metrics, and reporting. Advises senior leaders on AI regulations, deployment risks, investment priorities, and compliance while leading cross-functional initiatives and influencing stakeholders across Quality, Digital, Operations, and R&D.

Lead machine learning architecture and technical direction across multiple quarters while remaining involved in experimentation, quality, and production systems. Work across model post-training, personalization and recommendation, and agentic experiences. Define solutions for ambiguous problems, align cross-functional partners, drive execution, and safely bring ML systems into production.

Architect and ship large-scale ML and evaluation systems for Atlassian Rovo Chat. Define technical strategy for measuring helpfulness, correctness, reliability, and agentic capabilities; develop datasets, simulations, automated judges, experimentation platforms, and production quality signals. Identify failure modes, implement interventions, and measure customer impact. Lead cross-functional initiatives, establish reusable AI quality standards, and mentor engineers while balancing quality, safety, latency, reliability, and cost.

Embedded Data Scientist for Superhuman Mail, partnering with product, growth, engineering, and design teams to guide roadmap decisions. Responsibilities include defining product metrics, designing experiments, applying causal inference and machine learning, measuring AI feature quality, analyzing activation and retention, identifying growth and conversion drivers, and communicating insights to stakeholders. The role requires strong Python, SQL, experimentation, statistics, and cross-functional influence skills.

Leads GitLab’s Product Data Science team, setting its strategy, roadmap, analytical standards, and priorities. Partners with Product and Engineering leaders on experimentation, causal inference, analytics, and production modeling. Manages, hires, coaches, and retains data scientists while building a high-performing function. Provides hands-on technical review of models, analyses, and queries, and communicates insights and risks to senior leaders.