Machine Learning Jobs in Oakland, CA

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Analyzes workforce and healthcare data to identify trends, build predictive and machine learning models, and deliver actionable recommendations. Uses Python, Alteryx, and Power BI for analytics, visualization, and reporting. The role supports workforce strategy, employee engagement, organizational effectiveness, talent management, and healthcare cost insights. Responsibilities also include client collaboration, relationship management, team guidance, and applying risk adjustment and medical economics concepts to inform strategic decisions.

Leverage advanced analytics, data manipulation, visualization, statistical modeling, predictive analytics, and machine learning to generate client insights and recommendations. Apply Adobe Experience Cloud solutions to support business transformation and strategic decision-making. Build client relationships, manage and inspire teams, navigate complex engagements, uphold professional standards, and deliver quality outcomes while developing technical and consulting expertise.

Leads data analysis and advanced analytics workstreams for clients, using statistical modeling, machine learning, data visualization, and Adobe Experience Cloud technologies to improve operations and strategic decision-making. Manages project planning, budgeting, execution, and completion while mentoring teams, maintaining data integrity and security, building Tableau and Power BI dashboards, identifying business opportunities, and integrating innovative analytics practices.

Supports data and analytics consulting projects through research, data analysis, visualization, predictive analytics, and machine learning tasks. Uses Python, SQL Server Data Tools, Power BI, Tableau, and Alteryx to manipulate data, validate findings, develop visualizations, and generate actionable insights. Collaborates with experienced professionals while learning consulting methodologies, artificial intelligence applications, and data-driven solution development.

Leads internal audit and SOX engagements, manages client relationships, develops audit methodologies, evaluates financial statements and internal controls, conducts risk assessments, and oversees audit reporting. The role includes project ownership, budgeting, stakeholder management, team leadership, mentoring, process improvement, and use of AI and risk technology to enhance audit effectiveness and address compliance and governance risks.

As a Staff Machine Learning Engineer, you will enhance credit modeling, manage full credit modeling stacks, and support innovations within regulated banking frameworks.

Founding Head of Talent responsible for full-cycle recruiting across science, engineering, operations, and global health roles. The role includes defining positions and interview processes, building talent pipelines, closing senior candidates, establishing recruiting infrastructure, and developing leveling, offer, and onboarding foundations. The leader will advise founders on talent strategy, organization design, and compensation while potentially building a recruiting team.

Serve as the technical sales and customer-facing expert for an AI platform. Responsibilities include conducting product demonstrations, supporting proofs of concept, advising on architecture and development, planning professional services projects, tuning retrieval and agentic pipelines, responding to RFPs, supporting customer adoption, and communicating product feedback internally. The role requires strong AI, cloud, on-premise, application development, storytelling, and enterprise customer engagement experience.

Own the full product lifecycle for AI-native insurance agencies and loss prevention services, spanning customer experiences, internal tools, automation, and integrations. Rapidly prototype MVPs with AI-assisted tools, define success metrics, analyze quantitative and qualitative feedback, align cross-functional stakeholders, and ensure regulatory and data security compliance. The role reports to the CEO and requires autonomous product ownership in a fast-moving startup environment.

Annotate and manage training/test datasets, run model training, evaluate model performance, perform manual QA and error analysis, and refine annotation guidelines for NLP/ML projects.

Conduct original philosophical and AI-alignment research, identify important research questions, connect conceptual analysis with empirical and technical work, design experiments and evaluations, collaborate across disciplines, and communicate findings through papers, reports, presentations, and external engagement. Depending on seniority, the role may also define research areas, shape the agenda, lead collaborations, and mentor researchers.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, LTV, and next-best-action decisioning. Design reliable signal contracts covering freshness, provenance, confidence, eligibility, and calibration. Lead feature pipelines, model serving, experimentation, monitoring, and feedback loops while evaluating fairness, risk, compliance, trust, and long-term customer impact. Collaborate across product, growth, data, platform, modeling, risk, and compliance teams.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, lifecycle intelligence, and next-best-action decisioning. Design reliable signal contracts with freshness, provenance, confidence, and calibration guarantees. Lead experimentation, monitoring, feedback loops, and impact evaluation focused on fairness, trust, risk, compliance, and long-term engagement. Partner across product, growth, data, platform, modeling, risk, and compliance teams.

Consultative Sales Engineer responsible for driving complex sales opportunities, designing cloud data security and data risk management solutions, delivering proof-of-value engagements and technical demonstrations, advising executive stakeholders, supporting partner channels, and coordinating cross-functional resources. The role requires cloud security, IAM, networking, databases, encryption, privacy, data governance, and strong technical communication skills.

Updates machine learning training and test datasets, refines annotation guidelines, initiates model training, and evaluates model performance. The role annotates synthetic text and image data, conducts manual quality analysis, identifies error patterns, reports anomalies, and produces findings on utterance quality, LLM evaluation, ASR bugs, and customer pain points. It also applies basic NLP and linguistic knowledge to ensure data accuracy and consistency.

Write and edit accessible journalism about AI for a general audience. Responsibilities include pitching ideas, drafting articles, editing researchers’ and writers’ work, setting editorial standards, and helping launch a consistent media program. The founding editor will shape editorial direction and clarify technical material without oversimplifying it.

Drive data-informed decisions across carrier growth, product launches, user acquisition, and partnerships. Partner with marketing, product, and business development teams; design and analyze A/B experiments; identify scalable growth levers; conduct complex analyses using machine learning models; and automate manual processes to improve operational efficiency.

The Business Operations Associate will drive data-informed decisions across growth, product, and business initiatives. Responsibilities include analyzing customer and partner performance, designing and evaluating A/B experiments, identifying profitable acquisition and partnership opportunities, leveraging machine learning models for insights, and automating manual processes to improve operational efficiency. The role partners with marketing, product, and business development teams and communicates findings to stakeholders ranging from frontline employees to executives.

Lead data-driven business operations and analytics initiatives across acquisition, partnerships, and product growth. Design and analyze A/B experiments, use machine learning and SQL for complex analyses, identify scalable growth levers, inform strategic decisions, and automate manual processes to improve operational efficiency. The role partners with marketing, product, and business development teams and communicates insights from frontline teams to the C-suite.

Drive data-informed decisions across growth, product, and business initiatives. Partner with marketing, product, and business development teams; design and analyze A/B experiments; identify acquisition and partnership growth levers; use advanced machine learning models for business analysis; and automate manual processes to improve operational efficiency. Communicate insights and recommendations to stakeholders ranging from frontline employees to executives.

Design control-system requirements and architecture for a DNA-origami electrostatic actuator-sensor system. Define sensor and actuator constraints, review FET and drive-stack designs, specify experimental data capture for system identification and machine learning, and develop a closed-loop control, simulation, and estimation plan. Provide a documented handoff to the incoming Controls engineer while advising physics and wet-lab teams on a fractional contract basis.

Develop and operate machine learning systems for automated manufacturing. Responsibilities include building scalable data and model infrastructure, automating ML pipelines, deploying models to production and edge devices, monitoring performance, optimizing latency, integrating AI/ML tools, managing incidents, ensuring security and privacy, and collaborating with automation engineers, data scientists, data engineers, and software teams.

Build and operate production MLOps infrastructure spanning experimentation, model training, deployment, serving, monitoring, retraining, and rollback. Develop ML data and feature pipelines using multimodal manufacturing data, maintain lakehouse and feature-store capabilities, and establish observability and human-in-the-loop feedback systems. Collaborate with data, AI, software, process, materials, and simulation engineers to deliver reliable, scalable ML platforms and model feedback loops.

Build foundational AI-enabled applications, prototypes, prompts, tests, documentation, and agentic automations. Translate defined requirements into software components, scripts, integrations, and workflow configurations. Support prompt engineering, retrieval-assisted use cases, API integrations, testing, troubleshooting, documentation, code reviews, and demonstrations. Apply software development lifecycle practices, data privacy, information security, compliance, and responsible AI standards while learning from senior team members.