Top Machine Learning Jobs in Sacramento, CA

Leads high-visibility product programs focused on market adoption, analyst relations, customer advisory boards, executive briefings, and industry events. Translates technical product roadmaps into compelling market value propositions, synthesizes analyst and customer feedback, and provides actionable insights to product leadership. Partners cross-functionally with Product, Marketing, and executive stakeholders while supporting strategic initiatives and external-facing communications.

Leads technical customer engagements for strategic enterprise accounts, combining solution engineering, software implementation, consulting, and sales support. Responsibilities include discovering requirements, designing and documenting SailPoint solutions, conducting foundational implementations and proof-of-value engagements, educating customers on architecture and best practices, and developing AI-enabled demonstrations and agentic solutions. The role collaborates across sales, product, professional services, partners, and customer success, and may require up to 15% travel.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

Engineer AI-powered threat detections, automated response playbooks, and continuous adversary emulation. Apply machine learning and LLMs to security operations, conduct threat hunting and AI red teaming, map coverage to MITRE ATT&CK, and develop detection-as-code through CI/CD. Build production-grade Python integrations, measurable containment capabilities, and safe autonomous defenses with guardrails. Mentor security professionals and collaborate across threat response, cyber defense engineering, and platform teams.

Design, build, and operate high-throughput, low-latency ad-serving infrastructure processing millions of requests per second. Optimize GPU-powered TensorRT inference, feature stores, model serving, benchmarking, experimentation tooling, and system reliability. Partner with machine learning engineers to productionize models and own systems through design, deployment, observability, capacity planning, incident response, and optimization.

Leads AI- and data-driven manufacturing transformation strategies for Life Sciences clients. Advises C-suite and operations leaders, develops AI strategies, business cases, roadmaps, operating models, and responsible AI plans, and oversees large-scale greenfield and brownfield manufacturing programs. Drives digital twin, automation, data platform, advanced analytics, generative AI, and AI/ML deployments from proof of concept through enterprise scale. Builds executive relationships, originates opportunities, manages partner ecosystems, develops manufacturing capabilities, and leads teams, delivery, financial performance, and risk.

Design and scale silicon spin-qubit processors from PDK development through tapeout, fabrication, validation, and system integration. Responsibilities include qubit standard-cell design, automated placement and routing, signal integrity and thermal analysis, customized EDA capabilities, PDK collateral, design-rule validation, interposer interfaces, and electrical characterization structures. The engineer collaborates with quantum, control, packaging, process, lithography, software, and external customer teams to enable large-scale quantum processor designs.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Leads advanced device development for Intel Foundry semiconductor process technologies. Manages device engineers, directs experiments and electrical or physical data analysis, develops test structures, validates design rules, supports PDK delivery, and collaborates across integration, modeling, reliability, and foundry engineering teams. Uses statistical analysis, data mining, machine learning, and fab data to guide process development and ensure performance, yield, reliability, and high-volume manufacturing readiness.

Develop scalable AI applications, integrate and orchestrate LLM workflows, engineer prompts, implement RAG, classify technical data, and build AI-powered developer tools such as VS Code extensions and command-line utilities. The role also requires Git collaboration, external data-source integration, monitoring, guardrails, data preprocessing, feature engineering, and classifier validation.

Lead end-to-end LLM inference performance across serving systems, models, GPU hardware, and workloads. Define rigorous benchmarks, diagnose bottlenecks from scheduling through kernels and interconnects, optimize single- and multi-node deployments, and deliver production-ready runtimes and configurations. Collaborate with product and infrastructure teams, evaluate inference technologies, and implement runtime fixes. The role requires strong Python engineering, production experience with serving engines such as vLLM or SGLang, GPU profiling, and knowledge of modern inference optimization techniques.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

Own and improve Headway’s privacy program across HIPAA, 42 CFR Part 2, state privacy laws, individual rights, incidents, vendor governance, and AI use. Maintain controls, data maps, metrics, training, and operational workflows. Conduct product, vendor, and AI privacy assessments; manage rights requests and incident investigations; coordinate notifications and regulatory submissions; and translate complex requirements into practical guidance for cross-functional teams.

Own the strategy and roadmap for patient-provider search, retrieval, ranking, and marketplace matching. Lead experimentation, objective functions, evaluation, and ML platform foundations while partnering with ML engineers, data scientists, design, clinical teams, and executives. Improve successful, lasting care matches rather than optimizing only clicks or bookings. Balance patient access, provider growth, clinical quality, and payer priorities, while setting the standard for technically grounded product management.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Define architecture and metrics for autonomous driving evaluation, lead cross-functional integration across autonomy and simulation teams, develop statistical and machine learning methods, analyze large-scale time-series data, manage evaluation KPIs and release gates, and communicate safety and readiness insights through dashboards and stakeholder presentations.

Serves as the technical bridge between customers, Sales, and technology teams. Leads technical discovery, solution architecture, demonstrations, workshops, proofs of concept, and evaluations using HERE data, APIs, mapping, routing, spatial analytics, cloud, AI, and enterprise integration. Partners with Sales to qualify opportunities, reduce technical risk, develop proposals and reusable presales assets, and guide customers toward location-intelligence solutions.

Analyze malware, binary files, customer detection tickets, and machine learning detections to improve detection efficacy and reduce false positives. Review product detections, assess binary legitimacy, address internal detection questions, and provide insights to data science teams. The role collaborates with technical account managers, incident response teams, data scientists, and malware researchers. Required capabilities include malware analysis, reverse engineering, Python scripting, binary fundamentals, threat assessment, research methods, operating systems, and AI-assisted workflow improvement.

Drives new business growth in mobility data and analytics markets by identifying opportunities, developing strategic plans, building partner and reseller relationships, negotiating cooperative contracts, and managing sales pipelines. Conducts market and competitor analysis, supports RFX processes, develops sales targets and budgets, and collaborates with internal teams and leadership. Represents the company at trade shows and conferences while serving as a subject matter expert for partners and resellers.

Leads enterprise-wide AI strategy, governance, use-case development, vendor evaluation, pilot programs, implementation, change management, risk mitigation, and executive reporting. Partners with leaders across construction, manufacturing, service, VDC, estimating, business development, and finance to identify and scale high-value AI and automation initiatives while measuring ROI and adoption.

Develop, test, and integrate AI software for aerospace and industrial applications. Interns will use Python, JavaScript, and C++ to build AI solutions, evaluate data and algorithms, collaborate with machine learning and data engineers, monitor AI trends and regulations, and present progress to leadership and stakeholders.

Design and lead the technical strategy for search across Vannevar’s products. Build and improve full-text, vector, and LLM-powered retrieval systems, optimizing latency, relevance, index quality, freshness, and performance. Collaborate with ML, product, design, and mission strategy teams to translate user needs into search innovations. Apply expertise in information retrieval, NLP, machine learning, indexing, ranking, query parsing, tokenization, vector search, and evaluation frameworks.

Design and scale automation and AI-driven solutions for security governance, risk, and compliance. Build pipelines to collect and validate evidence, integrate GRC platforms with security and IT systems, automate control monitoring and audit preparation, and deploy LLM-based workflows for testing, policy mapping, and risk reporting. Establish AI guardrails and quality controls while collaborating with security, engineering, data, and business teams to improve compliance and security posture.

Leads enterprise security strategy and operations across Microsoft MCAPS-Core. Oversees security programs, governance, risk management, threat intelligence, incident response, compliance, security reviews, performance metrics, budgets, and cross-organizational partnerships. Directs teams and suppliers, develops AI-enabled security capabilities, assesses geopolitical and operational threats, communicates executive-level risk insights, and ensures security controls are adopted without disrupting business or customers.

Lead the Content Foundations engineering team and evolve Scribd’s high-scale document ingestion platform into a real-time, AI-native system. Responsibilities include setting technical strategy, integrating AI and ML capabilities, overseeing distributed data infrastructure, ensuring security and operational reliability, managing the roadmap, hiring and developing backend and AI data engineers, and partnering with product and engineering leaders across the organization.