Machine Learning Jobs in Berkeley, CA

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 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.

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.

Develop software for clinical brain-computer interface applications, including BCI data curation, neural decoding algorithms, hardware-in-the-loop evaluation, and software tools. Collaborate with scientists, engineers, neuroscience, machine learning, and quality teams to define requirements, review code, and support clinical studies. Regular travel between Austin and Oakland offices is required.

Build and maintain scalable, reliable ML data platform systems to support dataset generation, model training, analytics, monitoring, and large-scale inference. Collaborate with ML, software, and infrastructure engineers to design cost-efficient data lake and training infrastructure using vendor and open-source tools to enable next-generation ML models.

As an Applied AI Engineer at Luminai, you will design experiments, optimize machine learning models, and enhance AI platforms for healthcare automation.

The Machine Learning Scientist will develop reinforcement learning algorithms for protein design, collaborate with teams, and shape machine learning strategies.

The Senior Manager, Product Lead will oversee a team of product managers, driving product strategy, talent development, and excellence in AI-powered legal products.