Top Scientist Jobs in San Francisco, CA

Deploys, optimizes, and scales production AI and agentic workloads for strategic customers on DigitalOcean’s AI-Native Cloud. Builds inference, runtime, orchestration, benchmarking, automation, and deployment frameworks; tunes GPU and distributed-system performance; validates new platform capabilities; and develops reusable reference architectures and operational playbooks. The role also partners with customers, vendors, model providers, and internal engineering teams to accelerate production adoption and platform maturity.

Design and implement production multi-agent AI systems for marketing compliance. Build ML pipelines, fine-tune models, develop prompt and evaluation frameworks, translate compliance rules into agent logic, and create monitoring, explainability, and feedback-loop infrastructure. Collaborate with engineering, product, implementation, and compliance teams to integrate AI features and improve customer-specific recommendations.

Conduct foundational and applied ML research on AI well-being and moral status, design and run experiments (including tooling/infrastructure), collaborate with external researchers, and publish papers, blog posts, and talks to inform AI developers, policymakers, and the public.

Research and develop agentic AI systems for cybersecurity, including LLM post-training, reinforcement learning, planning, tool calling, retrieval, memory, prompt optimization, and rigorous evaluation. Collaborate with cybersecurity experts, data scientists, and engineers to prototype state-of-the-art methods and transition successful systems toward production. Analyze agent failures, track AI advances, and define research priorities.

Lead data science efforts to inform product and business strategy through quantitative analysis, experimentation, forecasting, and storytelling. Partner with cross-functional teams to define hypotheses, measure impact, influence roadmaps, present insights to senior leadership, and monitor key product metrics to drive value.

The job description does not include specific responsibilities or qualifications. The role is identified as Principal Data Scientist and may be performed remotely, in an office, or in a hybrid arrangement, depending on the employee’s location and Atlassian’s legal entity availability.

Own measurement strategy and performance improvement for Snap’s advertising measurement solutions. Lead experimental design, causal analytics, benchmarking, meta-analyses, governance, and scalable measurement initiatives. Advise executives and external partners, influence product roadmaps, develop thought leadership, and build analytical fluency across teams. Apply advanced statistics, SQL, AI tools, dashboarding, and data visualization to guide advertising efficacy decisions and identify innovation opportunities.

Leads AI-enabled risk modeling services, setting strategy, managing client relationships, overseeing machine learning and predictive analytics solutions, and guiding end-to-end delivery. The role includes model governance, feature engineering, performance evaluation, innovation, cross-functional collaboration, team coaching, thought leadership, and business development across risk modeling engagements.

Analyze complex client problems using statistical modeling, exploratory analysis, data visualization, and machine learning. Develop AI and GenAI solutions, data workflows, pipelines, models, and quality checks that generate actionable business insights. Communate recommendations through documentation and visual storytelling, manage workstreams, collaborate with clients, and mentor junior team members.

Senior Data Scientist partnering with Product, Engineering, Design, Operations, and Clinical teams to define metrics, design experiments, apply causal inference, analyze funnels and cohorts, and build predictive models. The role translates complex data into actionable product and business recommendations, operationalizes validated insights, and establishes standards for experimentation and analytical quality across patient, payer, outcomes, and provider-focused teams.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Design and deploy production AI systems that transform large-scale geospatial data into actionable intelligence. Build end-to-end ML pipelines, multimodal and reasoning models, experimentation frameworks, synthetic datasets, and scalable inference systems. Collaborate with research, engineering, product, and domain teams while optimizing models for reliability, latency, and cost. Support monitoring, retraining, evaluation, and next-generation Earth AI capabilities.

Conduct independent robot-learning research from hypothesis through data, model training, deployment, and evaluation. Establish long-term research direction, experiment with novel architectures and post-training methods, develop data strategies, deploy models on physical robots, define real-world metrics, and build scalable machine-learning infrastructure for research and production.

Lead development, optimization, and scaling of advanced human tissue models for automated therapeutic discovery. Design rigorous experiments using primary human cells and 3D cultures, characterize tissues through imaging and multi-omic techniques, troubleshoot variability, and transfer exploratory workflows into scalable, production-grade systems. Collaborate with bioengineering, computational biology, AI, robotics, hardware, and translational research teams. The role requires a PhD, postdoctoral or industry experience, deep cell culture expertise, and the ability to independently drive complex R&D programs.

Conduct hands-on research on frontier AI models, reasoning, agents, and multimodal systems. Design benchmarks, evaluation frameworks, and experimental pipelines; rapidly develop prototypes, run experiments, identify model limitations, and deliver actionable findings. Collaborate with customers, engineering, and product teams to address complex research challenges. Preferred expertise includes AI cybersecurity, autonomous security agents, vulnerability research, LLM red teaming, adversarial machine learning, prompt injection, and agentic AI security.

Staff Data Scientist partnering with Growth, Marketing, Product, and Engineering teams to build data science solutions that drive customer acquisition, engagement, and retention. Responsibilities include predictive modeling, personalization, recommendation systems, LLM applications, incentive optimization, causal inference, marketing impact measurement, technical mentorship, and strategic leadership. The role requires deploying production machine learning systems and communicating analytical insights to senior stakeholders.

Develops, optimizes, qualifies, transfers, and manages cell-based bioassays, potency assays, functional assays, and binding assays for biologic therapeutics. Performs mammalian cell culture, transfection, flow cytometry, plate-based testing, and orthogonal analyses. Evaluates assay performance, robustness, relative potency, and critical quality attributes while preparing technical and regulatory documentation. Collaborates cross-functionally with discovery, process development, quality, regulatory, CDMOs, and external partners, and provides technical guidance to junior personnel and partners.

Designs and executes translational oncology experiments focused on brain tumors, using pharmacological, in vitro, in vivo, and molecular genetics approaches. Performs data analysis and interpretation, contributes to manuscripts, presentations, and grant applications, and works independently under principal investigator guidance. Desired expertise includes molecular biology techniques, patient-derived tumor cultures, mouse models, tumor sample processing, computational analysis, pharmacology, and cancer signaling pathways.

Lead payments-focused data science work: build foundational datasets, metrics, and scalable analytics; analyze customer and transaction behavior (including onchain data); conduct strategic analyses to identify product and revenue opportunities; build reporting, dashboards, and automation; and influence product and business strategy through data storytelling to senior stakeholders.

Leads a centralized gene-editing screening function supporting AI-driven protein design. Responsibilities include designing and troubleshooting mammalian genome-editing assays with NGS readouts, ensuring data quality, developing SOPs and acceptance criteria, managing sample tracking and cell inventories, coordinating cross-functional workflows, improving high-throughput processes, and hiring and mentoring Research Associates.

Lead cheminformatics for drug discovery programs, applying machine learning, QSAR, ADME modeling, chemical data analysis, and predictive scoring to guide compound design. Collaborate with medicinal chemists and biologists on library design, hit-to-lead, and lead optimization. Build reproducible computational infrastructure, curate public chemical datasets, and develop agentic tools that help scientists propose, triage, and analyze compounds.

Conduct controlled fine-tuning and reinforcement learning experiments to understand how training data affects model behavior. Develop evaluations and training datasets, assess noisy experimental results, improve data quality, publish technical findings, and present evidence to internal and external partners across domains including finance, software engineering, and policy.

Research Engineer or Scientist developing reinforcement learning algorithms and systems for frontier AI models. Responsibilities include researching policy optimization, asynchronous RL stability, scaling behavior, controlled experiments, evaluations, training recipes, and distributed RL frameworks. The role also involves extending prime-rl, improving numerical correctness and performance, and diagnosing unstable training runs and regressions.

Lead a research program focused on continual learning, model adaptation, self-improvement, and memory. Develop experiments, collaborate with ML and software engineers, and transition research into production features. The role requires applied research experience, post-training expertise for large models, strong Python and deep learning skills, distributed training experience, and publications at leading venues. This is an on-site senior individual contributor position in a fast-paced startup environment.

Lead applied machine learning research from ideation through production deployment. Develop predictive, optimization, and language-model-based systems; curate specialized datasets; retrain models as conditions change; and conduct large-scale experiments and data engineering. The role requires strong Python skills, quantitative modeling experience, and a doctoral degree in a relevant discipline.