Top Machine Learning Jobs in Sacramento, CA

Leads AI data annotation operations, managing workflows, resources, production targets, quality standards, audits, and performance metrics. Oversees annotation teams through coaching, recruitment support, onboarding, and performance management. Partners with AI engineers, trainers, product teams, and operational stakeholders to translate requirements into plans. Drives process improvement, automation, tool adoption, data integrity, and operational excellence across AI and machine learning data preparation initiatives.

Leads the strategy, roadmap, development, launch, and optimization of AI-powered products. Defines product vision, requirements, priorities, success metrics, and adoption goals while coordinating engineering, AI, business, executive, and customer stakeholders. Evaluates emerging AI capabilities, supports responsible AI governance, manages product performance, and drives continuous improvement aligned with business objectives.

Identifies business opportunities for AI and automation, gathers and documents requirements, develops business cases, supports solution design and delivery, facilitates stakeholder collaboration, assists with testing and change management, and tracks project outcomes, reporting, and benefits realization.

Owns AI product backlogs, priorities, requirements, user stories, and acceptance criteria. Translates business needs into technical solutions, coordinates stakeholders and delivery teams, supports Agile ceremonies and product releases, monitors adoption and performance, and drives continuous improvement. The role also identifies AI-driven business opportunities, supports responsible AI practices, and contributes to AI product strategy.

Tests and validates AI-powered products, machine learning models, and Generative AI applications. Designs test strategies, executes functional, regression, and user acceptance testing, validates model outputs, identifies defects, analyzes quality metrics, documents evidence, and supports compliance and release readiness. Collaborates with AI engineers, product managers, and business teams to improve testing methodologies, quality frameworks, and AI evaluation practices.

Designs, implements, optimizes, and administers generative and agentic AI solutions. Translates business requirements into AI workflows, oversees model deployment and performance, analyzes implementation data using Power BI, and coordinates responsible AI governance. Collaborates with engineering, security, data science, sales, and reporting teams while supporting users and maintaining AI platforms, cloud services, and low-code tools.

Owns intake and triage for marketing technology requests, learns marketing tools and data flows, and designs automation workflows with engineers and data scientists. Builds solutions using internal frameworks and low-code tools, tests variable AI outputs, guides user acceptance testing, documents processes, enables marketing teams, and measures business impact. The role connects marketing needs with agentic workflows, integrations, custom applications, and ML-based analysis.

Own the technical systems that convert market signals into revenue pipeline. Build and maintain prediction models, multichannel campaign infrastructure, AI-driven personalization, call intelligence, and signal-to-task workflows. The role requires production coding, applied machine learning, API integrations, SQL, CRM and GTM stack expertise, and practical LLM usage. You will manage live revenue systems, improve attribution and data quality, and balance rapid experimentation with reliability.

Analyst supporting a marketing engineering team by translating marketing needs into automated workflows, integrations, internal applications, and AI-enabled solutions. Responsibilities include intake and prioritization, process analysis, workflow design, low-code development, collaboration with engineers and data scientists, testing variable AI outputs, user acceptance testing, documentation, enablement, and impact measurement.

Own the full sales cycle for commercial accounts, from outbound prospecting and discovery through closing and implementation handoff. Build relationships with technical and business stakeholders, manage Salesforce pipeline data, forecast accurately, plan accounts, and achieve revenue targets. The role requires consultative B2B SaaS selling and the ability to explain technical AI, machine learning, data science, and open-source solutions. This is a fully remote position restricted to candidates in Central, Mountain, or Pacific time zones.

Develop and maintain quantitative models for forecasting, pricing optimization, and risk assessment. Analyze mortgage business metrics to identify revenue, profitability, and operational improvement opportunities. Build performance measurement frameworks, dashboards, automated reports, and SQL-based analytics workflows. Use Python for statistical analysis, automation, and machine learning workflows. Validate and monitor model performance, recalibrating models as needed, while communicating actionable recommendations to business leaders.

Own the strategy and roadmap for Constructor’s search quality and Product Discovery Engine. Drive measurable improvements in relevance, conversion, revenue per visit, and GMV across retail verticals. Partner with ML, data science, and engineering teams to productionize state-of-the-art retrieval, ranking, query understanding, personalization, and LLM capabilities. Establish evaluation frameworks, investigate quality issues, align multiple teams, and expand into chapter-wide product leadership.

Develop statistical, machine learning, and deep learning models to measure the impact of pharmaceutical HCP, DTC, and Medical Affairs campaigns. Partner with internal and external clients, provide analytical insights and methodological guidance, consult with sales and market research teams, and coach an offshore analytics team. The role requires applying causal inference, healthcare and pharmaceutical data expertise, and clear communication of findings to clients and executives.

Analyzes, designs, develops, and maintains Java-based eCommerce systems, web applications, payment-processing integrations, APIs, and distributed infrastructure. The role also builds AI and machine-learning tools for transactional and operational data analysis. Responsibilities include architecture evaluation, performance tuning, security and PCI DSS compliance, testing, documentation, CI/CD maintenance, troubleshooting, and Agile collaboration.

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.

Lead the design and implementation of critical Safety AI initiatives, focusing on real-time perception systems for edge devices and optimizing models for production deployment in safety-critical environments.

Leads analytical and scientific strategy for Fetch’s Play and Rewarded Apps businesses. Develops predictive and machine learning models, experimentation frameworks, causal analyses, feature pipelines, and measurement systems using Python and SQL. Partners with Product, Marketing, Engineering, and business leaders to improve monetization, engagement, retention, personalization, and marketplace outcomes. Translates complex findings into strategic recommendations, mentors data scientists and analysts, and establishes analytical best practices.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Own the full machine learning lifecycle for search ranking and retrieval, including model training, deployment, serving, feature pipelines, evaluation frameworks, hybrid lexical-vector retrieval, large-scale embedding inference, query understanding, and permissions-aware multi-tenant search. Collaborate with infrastructure, AI, and backend teams to improve search relevance across ClickUp’s platform.

Leads the enterprise analytics organization, managing Analytics Engineers, Data Analysts, and BI Developers. Defines analytics strategy; delivers executive dashboards, KPI scorecards, operational reporting, semantic models, and self-service BI. Establishes data governance and metric standards, ensures data quality, partners with Engineering, Product, Finance, Operations, and executives, and translates business questions into actionable analytical solutions. Mentors analytics talent and drives prioritization, adoption, and measurable business outcomes.

Senior backend engineer responsible for setting technical direction, designing scalable distributed systems, building customer-facing web applications, mentoring engineers, and applying machine learning to large-scale healthcare data. The role requires Java, Scala, or Rust expertise, cloud systems experience, technical design leadership, cross-functional collaboration, and adoption of agentic engineering practices.

Leads Alkami’s enterprise and technology risk management program, including risk assessments, control evaluations, mitigation, monitoring, reporting, and escalation. Owns AI, machine learning, and generative AI governance, translating regulatory and risk expectations into practical policies, controls, and review processes. Partners with cross-functional risk owners and supports compliance initiatives involving business continuity, disaster recovery, regulatory management, and incidents. Reports to the Chief Compliance Officer in a fully remote role.

Provides product and technology legal counseling throughout the product lifecycle while operating Horizon3’s global privacy program. Advises Product, Engineering, Security, Compliance, and GTM teams on privacy laws, AI regulation, data flows, DPAs, cross-border transfers, privacy-by-design, incidents, audits, and customer diligence. Drafts playbooks, policies, notices, and contractual terms; supports enterprise negotiations; and delivers practical, risk-based guidance in a fast-moving cybersecurity environment.

Leads large-scale AI transformation programs for Cresta’s strategic enterprise customers. Serves as executive sponsor and trusted advisor to C-suite stakeholders, manages matrixed implementation and engineering teams, oversees complex deployments, standardizes delivery governance, and drives measurable customer value, adoption, expansion, and advocacy. Partners with Sales, Customer Success, Product, and Engineering while connecting technical outcomes to business ROI.