Santa Clara, CA

Data Jobs in Santa Clara, CA

55 jobs found

Include remote jobs

Today

Senior Data Scientist

NVIDIA Santa Clara, CA

Santa Clara, CA

Today

$152k - $265k/yr Full time Senior Artificial Intelligence Data Processing

Apply Now

Job Description

NVIDIA is hiring a Senior Data Scientist for its Finance Data Science and AI team in Santa Clara, CA (onsite). In this role, you will integrate Data Science and AI solutions into financial workflows, helping translate analytics into measurable outcomes for both technical teams and business stakeholders. The position combines model development with practical experimentation across ML, Bayesian methods, simulation, optimization, and generative AI and agents.

Compensation: USD 152,000 - 264,500 per year. Expected base salary ranges include 152,000–230,000 USD for Level 3 and 168,000–264,500 USD for Level 4. Base salary is determined based on location, experience, and other objective, job-related factors.

Responsibilities

Apply business context alongside data science methods to support finance data science initiatives using traditional machine learning, Bayesian statistics, simulation, and optimization.
Integrate AI tools and Agents into data science workflows, experimenting with new technology to identify where it can create organizational value.
Translate technical solutions into business terms and convert business requests into technical plans, helping technical and nontechnical collaborators understand value, risks, and ROI.
Conduct Generative AI experiments to identify high-value organizational use cases, including automated auditing and predictive forecasting.

Requirements

5+ years of professional experience in business-centered AI and Data Science, including a demonstrated history of implementing models in production.
Hands-on experience building custom agents and multi-agent systems, with proficiency in AI-native development tools such as Cursor and Claude Code and experience with agent orchestration frameworks.
BS/MS/PhD (or equivalent) in Computer Science, Data Science, Electrical/Computer Engineering, Physics, Mathematics, or another Engineering field. Technical Master’s or Ph.D. with a finance or business background is preferred.
Strong technical skills for designing, validating, deploying, and maintaining data science models using Python, SQL, and Databricks (or similar).
Data science project management experience, including close collaboration with multiple stakeholders and coordination of organizational resources.
Excellent communication skills, with ability to document and present complex findings and project status to both technical and business collaborators.

Technologies

Python
SQL
Databricks
Cursor
Claude Code

Benefits

Equity
Benefits

Ways to Stand Out

Operations finance or other applied, business-focused data science experience, especially at a larger company or in the semiconductor/tech industry.
Evidence of creative, data science and agentic solutions that address legacy bottlenecks, improve decision making, and add business value.
Experience with NVIDIA software libraries and GPUs, plus OpenACC and CUDA optimization.

Applications: Accepted at least until November 3, 2026.

View full job page →

1d ago

New College Grad - Computational Physics & Agentic AI Engineer, Doctorate Degree (Spring 2027)

Applied Materials Santa Clara, CA

Santa Clara, CA

1d ago

$138k - $190k/yr Full time Agentic Ai Ai Agent Artificial Intelligence

Apply Now

Job Description

Applied Materials is seeking a New College Grad computational physics/plasma modeling and agentic AI engineer to help build physics-based simulation tools and digital-twin workflows for semiconductor process chambers. The role combines multiphysics modeling, verification and validation, and AI-enabled automation to improve both predictive performance and usability for engineering teams.

Role Focus

This position centers on three core areas of work:

Modeling Capability — develop and apply advanced multi-scale and multi-fidelity models for thermal and plasma-based semiconductor processes, connecting high-fidelity solvers with reduced-order and surrogate approaches, with quantified accuracy and defined limits of validity.
Verification & Validation — establish rigorous methods to verify computational models and AI-enabled workflows against reference solutions and validate predictions against experimental data, including the infrastructure required to maintain reliability as capabilities evolve.

Responsibilities

Apply computational modeling to semiconductor process and equipment development challenges, including thermal, plasma, deposition, etch, and related multiphysics applications.
Develop, implement, and improve physics-based simulation capabilities, numerical methods, and engineering workflows.
Use simulation, experimental, and engineering data to analyze process and hardware behavior, identify key physical mechanisms, and support design decisions.
Develop and evaluate reduced-order, surrogate, and machine-learning models when they improve computational efficiency or predictive capability.
Build AI-enabled tools and agentic workflows that help engineers set up, execute, analyze, and interpret computational models.
Collaborate with process, hardware, experimental, and software teams to define modeling problems, interpret results, and translate modeling insights into engineering decisions.
Verify and validate modeling and AI-enabled capabilities using appropriate reference solutions, simulation benchmarks, and experimental data.
Contribute to the development and maintenance of internal modeling software, reusable workflows, documentation, and best practices.
Communicate modeling results, assumptions, limitations, and recommendations clearly to scientists, engineers, technical leadership, and customers.

Required Qualifications

MS or PhD in Mechanical Engineering, Electrical Engineering, Aerospace Engineering, Chemical Engineering, Materials Science, Physics, or a related field.
Demonstrated experience in computational physics or multiphysics modeling, including one or more areas such as transport phenomena, fluid dynamics, heat transfer, chemistry, thermal-structural mechanics, plasma dynamics, or feature-scale process modeling.
Strong background in applied mathematics and computational methods, including numerical methods, statistical modeling, uncertainty quantification, or computational solver development.
Strong programming experience in Python and/or C++, including experience developing scientific or engineering applications.
Experience with machine learning for scientific or engineering applications, including surrogate modeling, physics-informed AI, neural operators, or related data-driven approaches.
Experience working with simulation data and computational models to analyze complex physical or engineering systems.
Strong technical judgment and attention to detail.
Excellent written and verbal communication skills.

Technologies

Python, C++, machine learning
Surrogate modeling, physics-informed AI, neural operators
LLM-based agents
High-performance computing, parallel computing, GPU acceleration
ACE+, COMSOL, ANSYS Fluent, OpenFOAM

Position Details

Location: Santa Clara, CA (onsite)
Time Type: Full time
Employee Type: New College Grad
Salary: USD 138,000 - 190,000 per yearly
Travel: Yes, 10% of the Time
Relocation Eligible: Yes
Education: MS or PhD

View full job page →

3d ago

Senior Data Analyst, Capacity Management

NVIDIA Santa Clara, CA

Santa Clara, CA

3d ago

$128k - $213k/yr Full time Senior Analytics Business Analytics

Apply Now

Job Description

NVIDIA’s DGX Data team is seeking a Senior Data Analyst, Capacity Management to help strengthen capacity planning and operational performance across DGX Cloud. In this onsite role in Santa Clara, CA, you will build analytics models, forecasting views, and dashboards that support infrastructure readiness and leadership decisions.

This position offers a USD 128,000 - 212,750 annual salary range, along with equity and benefits.

What you’ll do

Build and maintain analytical models for capacity demand, supply, utilization, allocation, and operational performance across DGX Cloud.
Develop forecasts and scenario analyses to inform business planning and infrastructure readiness.
Turn ambiguous business questions into structured analyses, datasets, dashboards, and actionable recommendations.
Develop and implement data-driven strategies to optimize prioritization and support flawless project execution.
Collaborate with cross-functional teams to identify and resolve supply chain issues.
Present findings and recommendations to stakeholders, including senior leaders.
Craft and maintain dashboards and reports to track key performance indicators (KPIs).
Apply advanced analytical techniques to forecast demand and supply trends.

What you bring

Bachelor’s degree in Engineering, Data Science, or a related field (or equivalent experience).
Minimum 8 years of data analysis experience, preferably in supply chain or capacity planning roles.
Demonstrated ability to implement data-driven strategies and solutions.
Strong analytical and problem-solving skills.
Proficiency in SQL, Python, and data visualization tools such as Tableau.
Strong communication skills, including presenting complex insights to collaborators.
Diligent, highly organized approach with a strict focus on accuracy.

Technologies

SQL
Python
Tableau

Ways to stand out

Advanced degree or equivalent experience in a relevant field.
Experience working in a fast-paced, high-tech environment.
Understanding of AI and machine learning applications.
Proven track record of leading data analysis projects to completion.
Strong project management skills.

View full job page →

3d ago

Sr Data Engineer

Baker Tilly Advisory Group, LP Santa Clara, CA

Santa Clara, CA

3d ago

$185k - $230k/yr Full time Backend Developer Artificial Intelligence Azure

Apply Now

Job Description

The Sr Data Engineer will join the founding team of Baker Tilly’s AI Product Group (APG) to build scalable, secure, and reliable data infrastructure that supports AI applications and intelligent agents.

Role Overview

This position centers on governed data foundations and production-grade data pipelines designed for AI use cases. You will work across lakehouse architecture, knowledge graph and retrieval capabilities, and evaluation-driven data engineering, with an emphasis on data quality, lineage, and operational reliability.

Responsibilities

Design, develop, implement, and maintain scalable batch and incremental data pipelines that transform structured and unstructured information into clean, validated, business-ready datasets within a modern lakehouse environment.
Build and optimize lakehouse architecture, including data processing and transformation layers that apply modern principles such as medallion architecture, data modeling, and integration of enterprise and legacy data sources.
Implement automated validation, reconciliation, lineage tracking, and metadata management to support data accuracy, consistency, traceability, and reliability across the data lifecycle.
Develop data models, data contracts, and entity-resolution capabilities to establish consistent client, engagement, and document identities across enterprise data sources.
Support data governance and access controls through data cataloging, classification, permissions, and access management for sensitive information used by authorized users and AI agents.
Build and maintain knowledge graph structures, data relationships, search indexes, and retrieval pipelines that enable AI applications to access relevant, accurate, and properly governed enterprise information.
Create pipelines that capture AI agent activity, practitioner feedback, reviews, and corrections to support ground-truth datasets, AI evaluation, and continuous improvements in data and retrieval quality.
Establish evaluation-based data engineering practices, including quality standards, testing frameworks, benchmarks, and validation processes for extraction, retrieval effectiveness, and grounding accuracy.
Maintain data freshness and reliability using incremental processing, change data capture, versioning, and monitoring so data products, knowledge graphs, and search indexes remain current as sources evolve.
Deploy, monitor, troubleshoot, and maintain production data pipelines using engineering best practices such as automated testing, code reviews, and operational documentation.
Collaborate with external engineering partners to transition data solutions into internally owned environments, including reusable code, tests, and runbooks.
Partner with AI Engineering, Platform & Production Engineering, enterprise data teams, subject matter experts, and engineering teams in India to align solutions with enterprise standards and business objectives.
Participate in special projects and other assigned duties to support the firm’s AI and data platform capabilities.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related field; advanced degree preferred.
3 years of hands-on data engineering or software engineering experience, including demonstrated success developing, deploying, and maintaining production data pipelines.
Experience independently building and operating production pipelines on a modern lakehouse platform, with ownership of data processing, transformation, and operational reliability.
Experience implementing data quality, lineage, or data contracts in production pipelines, including automated testing, reconciliation, validation, or traceability practices.
Strong proficiency in Python and SQL, including the ability to develop, optimize, test, and maintain production-quality data engineering solutions.
Strong understanding of data modeling principles and the ability to design scalable, maintainable data structures for evolving requirements and source systems.
Experience designing and developing batch and incremental data pipelines for structured and unstructured enterprise data.
Solid software engineering fundamentals, including version control, automated testing, code reviews, debugging, and reliable, repeatable data processing workflows.
Ability to validate technical solutions with measurable outcomes such as automated tests, benchmarks, experiments, performance metrics, or comparative evaluations.
Strong preference for experience with modern lakehouse technologies such as Databricks, Apache Spark, Delta Lake, or medallion architecture.
Preference for experience with cloud-based data platforms, preferably Microsoft Azure.
Preference for familiarity with enterprise data governance capabilities including data catalogs, classification, access controls, lineage, and technologies such as Unity Catalog or comparable solutions.
Preference for knowledge graphs and related tooling such as graph databases, graph data modeling, vector search, hybrid search, or retrieval-based applications.
Preference for familiarity with unstructured data processing, including document extraction, metadata management, content chunking, and search indexing.
Preference for experience with entity resolution, master data management, change data capture, or incremental data processing.
Understanding of data provenance, traceability, and governance requirements in complex enterprise or regulated environments.
Excellent written and verbal communication skills, including documentation and collaboration with technical and nontechnical stakeholders.
Demonstrated ability to work with business subject matter experts and globally distributed stakeholders to translate requirements into reliable data solutions.
Ability to manage competing priorities and adapt quickly in a fast-paced, collaborative environment.
Experience using AI-assisted and agentic development tools to improve engineering productivity, or willingness to incorporate them into daily development practices.
No professional certifications or licenses required; prior experience with AI, machine learning, LLM systems, or knowledge graphs is not required.

Preferred Skills & Tools

Python, SQL
Databricks, Apache Spark, Delta Lake
Medallion architecture
Microsoft Azure
Unity Catalog
Knowledge graphs, graph databases, graph data modeling
Vector search, hybrid search, retrieval-based applications
Document extraction, content chunking, search indexing
Entity resolution, master data management, change data capture, incremental data processing
LLM systems

Location

Santa Clara, CA (onsite)

Compensation

USD 185,000 - 230,000 per year

National pay rate range: $185,000 – $230,000. In California: $185,000 – $230,000.

View full job page →

5d ago

Machine learning engineer II, AWS Inference Optimization

Amazon Development Center U.S., Inc. Santa Clara, CA

Santa Clara, CA

5d ago

$165k - $224k/yr Full time Artificial Intelligence AWS Aws Bedrock

Apply Now

Job Description

This role is for a Machine Learning Engineer focused on AWS inference optimization. You will work on improving inference performance for large language OSS models, with ownership of price-performance for Amazon LLM inference across SageMaker, Hyperpod, and Bedrock.

What You’ll Do

Develop model servers.
Build CUDA kernels in vLLM and SGLang.
Create services for GenAI optimization and benchmarking.
Create or modify a mid-size application or service.
Optimize large language OSS models such as Qwen, Gemma, gptoss, and others by improving inference stacks including vLLM and SGLang.
Perform performance tuning for GenAI inference on Amazon SageMaker, Amazon Bedrock, and Hyperpod.
Implement inference optimization techniques including Speculative decoding, Disaggregated Inference, and CUDA kernel tuning.
Own price-performance for Amazon LLM inference deployed on SageMaker, Hyperpod, and Bedrock.
Deliver production solutions that leverage existing academic and industrial research or apply practical, original thinking.
Deliver solutions into customer-facing products.

Key Technologies

vLLM, SGLang
Amazon SageMaker, Amazon Bedrock
Hyperpod
CUDA
C#, C++, Java, Perl
Transformer architecture

Requirements

3+ years of non-internship professional software development experience.
2+ years of non-internship design or architecture experience, including design patterns and reliability and scaling of new and existing systems.
1+ year of software development engineer or related occupational experience.
1+ year designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using C#, C++, Java, or Perl.
Bachelor’s degree (or foreign equivalent) in Computer Science, Engineering, Mathematics, or a related field.
Knowledge of machine learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques.

Preferred Qualifications

3+ years of full software development life cycle experience, including coding standards, code reviews, source control management, build processes, testing, and operations.
Bachelor’s degree in computer science or equivalent.

Education

Bachelor’s degree (Computer Science, Engineering, Mathematics, or related field).

Location and Work Mode

Santa Clara, CA (onsite)

Compensation

USD 165,200 - 223,600 per year.

Benefits

Sign-on payments
Restricted stock units (RSUs)
Health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance, option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
401(k) matching
Paid time off
Parental leave
Workplace accommodation or adjustment during the application and hiring process (including support for the interview or onboarding process)

Los Angeles County Applicants

Job duties for this position include:

Work safely and cooperatively with other employees, supervisors, and staff.
Adhere to standards of excellence despite stressful conditions.
Communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service.
Follow all federal, state, and local laws and Company policies.
Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position, including the abilities and responsibilities listed above, as well as the abilities to adhere to Company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation.
Pursuant to the Los Angeles County Fair Chance Ordinance, qualified applicants with arrest and conviction records will be considered for employment.

View full job page →

7d ago

Sr Data Analytics Engineer

Cloud Destinations LLC. Santa Clara, CA

Santa Clara, CA

7d ago

$60 - $63/hr Full time Anomaly Detection Artificial Intelligence Aws Data Engineering

Apply Now

Job Description

Cloud Destinations LLC. is hiring a senior data analytics engineer to design data pipelines and analytics architectures that make engineering and operational data reliable, usable, and actionable. This onsite role in Santa Clara, CA focuses on building scalable analytics, monitoring, and correlation capabilities, along with dashboards, anomaly detection, and data models across multiple platforms.

What you’ll do

Analyze CAE, manufacturing, test, telemetry, and operational datasets to support engineering and mission workflows.
Build analytics for Mission Control, covering signals such as yield, power, thermal, NVLink, diagnostics, inventory, and related engineering metrics.
Develop automated detection for anomalies, outliers, drift, clusters, and trends.
Correlate data across sources including SFC, TestLogs, BOM, diagnostics, shipping, and issue-tracking systems.
Create dashboards, reports, alerts, and drill-down analytics for deeper investigation.
Develop reusable datasets and data models that engineering teams can apply across use cases.
Partner with subject-matter experts to define meaningful metrics and detection criteria.
Support AI/agent-based analytics while keeping engineering decisions under human review.
Improve data quality, performance, and accessibility for downstream applications.

Required skills and experience

Advanced SQL
Python
Data analysis and transformation
Large-scale data processing
Data modeling
Dashboard and visualization development
Statistical analysis and anomaly detection
ETL / ELT pipelines
Experience working with time-series and manufacturing/test data
API-based data integration
Data Engineer experience: 10 years

Tools and technologies

Databricks
AWS
Splunk
Power BI
Microsoft Fabric
Pandas
PySpark
Machine learning
AI/LLM-assisted analytics
Agentic workflows
API-based data integration

Strongly preferred

Databricks, AWS, Splunk, Power BI, and Microsoft Fabric
Pandas / PySpark
Machine learning or pattern-detection experience
Manufacturing, test, telemetry, or semiconductor datasets
Experience correlating multiple enterprise data sources
AI/LLM-assisted analytics or agentic workflows

Location and schedule

In person
Santa Clara, CA
40.0 hours per week

Compensation

$60.00 to $63.00 per hour

View full job page →

7d ago

Senior Data Engineer - EDA Datacenter Analytics and Observability

NVIDIA Santa Clara, CA

Santa Clara, CA

7d ago

$184k - $357k/yr Full time Senior Analytics Big Data

Apply Now

Job Description

NVIDIA is building analytics-ready data platforms that turn complex observability and telemetry into trusted datasets for observability, reliability analysis, and capacity forecasting for EDA datacenters. In this hybrid role in Santa Clara, CA, you will design and evolve pipelines that help teams move from raw metrics, logs, traces, and hardware health signals to analytics and predictive insights at scale.

What you’ll do

Design, build, and maintain analytics-focused data pipelines to ingest, transform, and curate observability data from EDA datacenters
Create reliable ingestion pipelines for metrics, logs, traces, and hardware health telemetry produced by large-scale CPU and GPU clusters
Partner with observability engineers to integrate data from Prometheus, Grafana, Elastic/OpenSearch, and Spark into unified analytical datasets
Model and organize data to support exploratory analysis, reliability modeling, forecasting, and long-term trend analysis
Build and optimize batch and streaming workflows for near-real-time analytics and historical analysis
Implement data quality checks, validation frameworks, and monitoring to support analytical accuracy and consistency
Define data retention, aggregation, and enrichment strategies that balance analysis needs, performance, and storage costs
Improve data discoverability, documentation, and usability to support self-service analytics
Work with data scientists and analysts to understand requirements and evolve datasets for new models and insights
Continuously improve pipeline scalability, reliability, and performance as datacenter footprint and workload complexity grow

What you’ll need

10+ years of experience designing, building, and operating large-scale data pipelines and data platforms for distributed systems or infrastructure data (MS preferred; BS acceptable with equivalent experience)
Proficiency in Python and SQL, including support for analytical and exploratory workloads
Hands-on experience with distributed data processing frameworks such as Spark (or similar)
Familiarity with observability and telemetry data, including metrics, logs, traces, and time-series data
Experience designing data models and schemas for flexible analysis and forecasting
Ability to take ownership of data engineering initiatives and drive them end-to-end with multi-functional partners
Experience implementing data quality, validation, and monitoring for analytics pipelines
Strong communication and collaboration skills, especially with engineering and infrastructure teams
Adaptability in fast-paced environments where analytical and operational needs evolve

Technologies you’ll work with

Python, SQL, Spark
Prometheus, Grafana, Elastic/OpenSearch
Time-series data, metrics, logs, traces

Compensation and benefits

Salary: USD 184,000 to 356,500 per year.

Benefits: equity and benefits.

Ways to stand out

Experience supporting datacenter infrastructure analytics, hardware reliability programs, or workload performance analysis
Familiarity with EDA workflows, HPC environments, or GPU-accelerated compute platforms
Experience integrating or operating observability stacks (Prometheus, Grafana, Elastic/OpenSearch, Kafka, Spark, or similar tools)
Background in large-scale distributed systems or data platforms
A track record of improving analytics velocity and reliability through better data foundations