Top Senior Machine Learning Jobs in Sacramento, CA

Leads the Product Acquisition engineering team and drives technology strategy, architecture, software delivery, DevOps maturity, platform adoption, vendor management, and operational excellence. Partners with product managers on roadmaps and business outcomes, ensures engineering standards and production support, and coaches, recruits, and develops engineers and consultants. Requires strong software development, product operating model, project management, stakeholder communication, and technical leadership experience.

Leads lab-in-the-loop biology programs that connect experimental design with machine learning model development. Oversees closed-loop experiments, model-output verification, perturbation studies, data generation, assay and model-system development, automation, metadata standards, CRO and vendor relationships, budgets, and cross-functional collaboration. Builds and mentors technical teams while aligning experimental and computational groups across a matrixed biotech organization.

Conduct research on frontier AI systems and develop datasets, experiments, prototypes, tooling, evaluation frameworks, and reinforcement learning environments. Investigate synthetic and agentic data generation, post-training, model understanding, benchmarks, and evaluation. Collaborate with research, engineering, product, and operations teams to translate findings into scalable AI applications, while contributing to technical publications, open-source work, mentorship, and the broader AI research community.

Conduct original research to evaluate, train, and improve frontier AI systems. Responsibilities include developing STEM benchmarks, synthetic data methods, reliability and hallucination evaluations, verification techniques, and agentic science workflows. The role involves identifying research gaps, designing rigorous experiments, building evaluation pipelines, publishing findings, and proposing new research directions across reasoning, AI evaluation, data generation, and AI-for-science.

Conduct frontier AI research and build research-grade datasets, prototypes, tooling, reinforcement learning environments, benchmarks, and evaluation frameworks. Investigate synthetic and agentic data generation, post-training, model understanding, and model capabilities. Design rigorous experiments, analyze results, and translate findings into scalable products and AI systems while collaborating across research, engineering, product, and operations teams.

Lead the development and production deployment of applied machine learning and LLM solutions across product, marketing, and operations. Own ranking, recommendation, filtering, personalization, experimentation, and model evaluation systems. Partner with business and technical teams to identify valuable use cases, establish modeling best practices, and ensure reliable production deployment. Manage and mentor applied ML practitioners while maintaining technical rigor, business alignment, and measurable customer and revenue impact.

Lead the design, development, deployment, and operation of production AI and ML systems. Build high-throughput Python services and Databricks pipelines, support traditional ML and LLM applications, establish testing and deployment standards, and develop monitoring for quality, reliability, latency, throughput, and cost. Partner with product, research, and responsible AI teams on governance and release criteria. Provide architectural leadership, conduct code reviews, mentor engineers, and resolve complex technical dependencies.

Leads and builds the mid-market business development function, owning territory strategy and the full sales cycle for pharma and biotech accounts. Develops and coaches a high-performing BD team, drives pipeline and new-logo acquisition, closes multi-year SaaS deals, and partners with marketing, strategy, product, and commercial operations. Represents the company at industry events and contributes to commercial frameworks, forecasting, segmentation, and coverage models.

Lead technical product strategy and execution for AI-powered learning intelligence infrastructure. Own roadmaps for AI curation, data ingestion, recommendation algorithms, learner profiles, analytics, and platform interoperability. Translate architecture, research, data, and business requirements into technical specifications, backlogs, success criteria, and implementation plans. Coordinate cross-functional stakeholders, manage delivery risks, and ensure systems are explainable, privacy-aware, validated, and aligned with educator and learner needs.

Manage a portfolio of B2B SaaS customers in the HR technology space, driving renewals, expansions, and revenue growth. Build strategic client relationships, conduct business reviews, monitor customer health, manage renewal and sales cycles, introduce products, and advocate for customers internally. The role requires strong sales acumen, analytical abilities, HR technology expertise, and experience influencing HR and L&D decision-makers. It also includes identifying adoption barriers, creating customer feedback loops, and mentoring others.

Leads an enterprise Account Executive team across a U.S. region, owning pipeline, revenue, forecasting, account planning, sales processes, onboarding, coaching, and growth targets. Supports strategic customer deals, partners with marketing on account-based campaigns, builds utility-industry relationships, and represents Neara at customer sites and industry events. The role also develops user groups and customer feedback loops to inform product direction.

Leads enterprise product evaluations, implementations, integrations, and adoption initiatives. Manages cross-functional projects involving AI, automation, system modernization, and third-party platforms. Translates business requirements into implementation plans, develops business cases and ROI analyses, coordinates vendors and stakeholders, prepares executive communications, assesses risks, and supports organizational change and user adoption.

Owns the intelligence platform roadmap for enterprise healthcare engagement, including decisioning, orchestration, integrations, interoperability, identity and consent management, and scalable deployment. The role defines strategy and requirements, prioritizes platform investments, leads partner integrations, establishes success metrics, and coordinates Engineering, AI/ML, Data Science, Security, Design, and client teams. It also embeds healthcare compliance, security, and responsible AI throughout the product lifecycle.

Provides strategic finance leadership to venture-backed AI and SaaS startups. Responsibilities include monthly close oversight, budgeting and forecasting, capital planning, treasury management, board reporting, investor updates, fundraising, M&A support, cap table analysis, revenue cycle management, cash flow planning, controls, audit readiness, and advising founders on financial strategy.

Build edge-focused full-stack product experiences integrating machine learning and autonomy for frontline defense users. Collaborate with designers, infrastructure teams, product teams, and ML engineers to create intuitive applications, efficient edge-deployed application stacks, and internal modeling APIs. Develop maintainable software using strong engineering practices, including design documents, code reviews, and style guides.

Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.

Leads enterprise product and technology integrations, cross-functional projects, AI and automation initiatives, business analysis, vendor coordination, executive communications, and change adoption. Develops requirements, implementation plans, business cases, risk strategies, and executive reporting while ensuring initiatives meet scope, schedule, compliance, and organizational objectives.

Lead the data science function, owning machine learning strategy, predictive models, healthcare analytics, DBT data marts, and executive dashboards. Analyze claims, eligibility, pharmacy, EMR, HIE, clinical, and operational data to identify high-risk patients, evaluate clinical interventions, and improve patient and provider engagement using AI and LLMs. Collaborate with clinical, operations, and technology teams while leading data science staff in a fast-growing healthcare organization.

Manage client relationships throughout implementation and ongoing support, serving as the primary client contact. Oversee KPIs, operational metrics, strategic planning, project execution, and client goal attainment. Translate client needs into platform opportunities, represent customer feedback to Product and Engineering, and develop case studies and success materials. The role requires healthcare client success or account management experience, strong communication, project leadership, and knowledge of medication management and healthcare quality measures.

Own customer-facing AI and data engagements from discovery through production launch. Define business value, MVPs, success criteria, and product requirements; prioritize engineering backlogs; lead technical discovery, architecture reviews, agile delivery, testing, production readiness, launch, adoption, and strategic handoff. Build C-suite relationships, translate business needs into technical requirements, resolve delivery risks, and identify future high-value opportunities.

Design and implement automated testing frameworks for data platforms, pipelines, distributed processing systems, and machine learning workflows. Develop strategies for data quality, schema integrity, drift, model accuracy, bias, regression, performance, and resilience. Investigate production failures, conduct exploratory testing, embed continuous validation into delivery processes, collaborate across engineering and data teams, and mentor junior SDETs.

Conduct computational discovery and optimization of rare-earth-lean and rare-earth-free permanent magnets. Execute DFT and multiscale simulations, develop machine-learning workflows and interatomic potentials, generate high-throughput materials data, and connect atomistic predictions to microstructure and manufacturing processes. Collaborate with experimental teams to validate models, contribute to publications, patents, project planning, and technical presentations, and support junior researchers.

Drive enterprise revenue growth for AI security products across Southern California. Develop strategic sales plans, build relationships with C-level stakeholders, lead complex negotiations, create customized proposals and demonstrations, manage CRM pipelines and forecasts, and represent the company at industry events. The role requires enterprise sales experience in cybersecurity or AI, strong knowledge of AI security concepts, and excellent communication, presentation, and negotiation skills.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

Leads the functional and technical design of AI-powered Oracle supply chain planning solutions. Translates demand, supply, inventory, MRP, and fulfillment workflows into production-ready agentic applications using OCI, Oracle AI Database, AI Agent Studio, and machine learning platforms. Responsibilities include data pipelines, master data optimization, OTBI and BI Publisher reporting, SQL/PL-SQL and Python development, LLM and RAG solutions, model deployment, integrations, architecture documentation, and delivery through CRP, SIT, and UAT.