Santa Clara, CA

Data Jobs in Santa Clara, CA

12 jobs found

Include remote jobs

1d ago

Senior Data Scientist, Cloud Gaming - Prescriptive Analytics and Optimization

NVIDIA Santa Clara, CA

Santa Clara, CA

1d ago

$184k - $288k/yr Full time Senior Analytics Big Data

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Job Description

This senior data scientist role focuses on prescriptive analytics and optimization for real-time cloud gaming, delivering scalable ML/AI and optimization solutions for routing, scheduling, and capacity management at NVIDIA.

Location

Santa Clara, CA (onsite)

Compensation

USD 184,000 - 287,500 per year

Experience and Education

Minimum 6 years of experience with a BS/MS or equivalent experience, or a PhD in Data Science, Computer Science, Operations Research, Statistics, Applied Mathematics, or a related quantitative field.

Responsibilities

Develop and deploy scalable ML, AI, and optimization models to improve demand forecasting, optimize capacity allocation, and implement user-specific feature engineering for real-time cloud gaming services.
Create reusable framework deployments for data ingestion, processing, and analysis to support dynamic user interventions toward targeted business outcomes.
Apply domain knowledge of the product and software stack to identify data inconsistencies and enhance model performance, especially in the context of optimization results.
Analyze complex datasets to identify trends and patterns using supervised and unsupervised learning to inform prescriptive solutions.
Design and enhance real-time prescriptive scheduling pipelines using linear programming and constraint optimization to improve capacity utilization and user retention.
Increase organizational productivity by mining petabytes of data for actionable insights, often delivered as prescriptive recommendations.
Collaborate with a diverse set of partners to gather requirements, design robust solutions, and guide the team toward impactful results.
Leverage agentic AI to deliver advanced automation and programming solutions for complex analytical problems.

Requirements

BS/MS (or equivalent experience) with 6+ years of experience or PhD in Data Science, Computer Science, Operations Research, Statistics, Applied Mathematics, or related quantitative fields, with a focus on prescriptive analytics and optimization.
Strong foundation in probability, statistics, AI/ML, prescriptive modeling, and optimization methodologies such as linear programming, network flow, decision theory, and multi-armed bandit.
Proficient coding skills, primarily Python, with the ability to write readable, testable, maintainable, and extensible code; experience with optimization libraries such as Google OR-Tools.
Experience with data storage and processing tools and the ability to address issues in running large-scale software across substantial clusters.
Solid experience in data cleaning, aggregation, transformation, and extraction, with an understanding of how data quality affects performance.

Technologies

Python
SQL
Delta Lake
Apache Spark
Databricks
MLflow
Grafana
Elasticsearch
Google OR-Tools
Kubeflow

Benefits

Equity
Benefits

What you will be doing

Develop and deploy scalable ML, AI, and optimization models to improve demand forecasting, optimize capacity allocation, and craft user-specific features for real-time cloud gaming services.
Build reusable data ingestion, processing, and analysis frameworks to support dynamic user interventions for targeted business outcomes.
Apply product and software stack knowledge to resolve data inconsistencies and boost model performance within optimization contexts.
Identify and interpret patterns in complex data using supervised and unsupervised methods to inform prescriptive solutions.
Enhance real-time prescriptive scheduling pipelines through linear programming and constraint optimization to maximize capacity utilization and retention.
Drive organizational productivity by mining vast data stores for actionable insights and prescriptive guidance.
Collaborate with partners to define requirements, craft robust solutions, and lead the team toward meaningful results.
Utilize agentic AI to deliver automation and programming solutions for challenging analytical problems.

What we need to see

BS/MS (or equivalent experience) with 6+ years of experience or PhD in Data Science, Computer Science, Operations Research, Statistics, Applied Mathematics, or related quantitative fields, with a strong emphasis on prescriptive analytics and optimization.
Solid background in probability, statistics, AI/ML, prescriptive modeling, and optimization methods (eg, linear programming, network flow, decision theory, multi-armed bandit).
Strong coding ability in Python with experience in optimization libraries such as Google OR-Tools; ability to write clean, maintainable code.
Experience with data storage and processing tools and handling large-scale distributed software across clusters.
Proficiency in data cleaning, aggregation, transformation, and extraction, with awareness of data quality impacts on performance.

Ways to stand out from the crowd

Strong collaboration and presentation skills for working with multiple partners, with the ability to explain complex analytical solutions and their business implications.
Experience in time series analysis and forecasting for demand prediction in optimization contexts is advantageous.
Experience operating active ML production pipelines (MLflow, Kubeflow) with a focus on deploying and monitoring optimization models is advantageous.

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2d ago

Supply Chain Data Engineer

NVIDIA Santa Clara, CA

Santa Clara, CA

2d ago

$152k - $288k/yr Full time Apache Airflow Automation Big Data

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Job Description

At NVIDIA, you will join a collaborative environment that emphasizes data-driven decisions and cross-functional partnership. This onsite role in Santa Clara offers the chance to own production-grade data pipelines, curate large datasets for forecasting and procurement, and influence unified planning decisions at the executive level. You will partner with modeling, procurement, operations, and IT teams, reporting to leadership. The compensation range for this role is USD 152,000 to 287,500 per year, with equity included as part of the benefits package.

Responsibilities

Design, implement, and scale automated data pipelines using SQL and Python to extract, transform, and load large supply chain datasets from both internal and external systems.
Data quality stewardship: develop automated validation scripts to detect anomalies, missing inputs, and historical inconsistencies before data enters planning frameworks.
Enhance pipelines that feed downstream machine learning and AI models, ensuring data is clean, low-latency, and prepared for advanced computation.
Infrastructure management: create and maintain optimized data tables, views, and schemas tailored for rapid querying by unified computational systems.
Systems deconstruction: collaborate to migrate legacy, decentralized planning workflows into automated, centralized data environments that link forecasting to procurement.
Cross-functional collaboration: work closely with engineering, global procurement, operations, and IT teams to uncover hidden data sources and standardize core supply chain metrics.

Requirements

Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Industrial Engineering, Operations Research, or equivalent experience.
Expert data engineering skills with mastery in SQL and Python, including data manipulation with Pandas.
7+ years of data pipeline experience, with a proven track record of building and running automated ETL/ELT pipelines in production.
Deep experience with relational databases, data warehouses (eg Snowflake, BigQuery), or large ERP systems (such as SAP or Oracle).
Extreme attention to detail and a strong focus on data integrity; thrives on resolving missing values and formatting issues in large datasets.
High autonomy and comfort working with flexible guidelines to craft robust, production-ready data pipelines from the ground up.

Technologies

SQL
Python
Pandas
Snowflake
BigQuery
SAP
Oracle
Apache Airflow
dbt
AWS
Azure
GCP
MATLAB

Benefits

Equity

Ways to stand out from the crowd

Experience building data infrastructure within semiconductor, electronics, or large-scale technology hardware supply chains.
Background with mathematical modeling environments, advanced computation engines, or algorithmic simulation software (eg MATLAB, advanced Python packages).
Familiarity with workflow orchestration tools (such as Apache Airflow or dbt) or infrastructure used to support Machine Learning pipelines (DataOps).
Experience cloud-architecting supply chain master data across AWS, Azure, or GCP.

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7d ago

Applied Machine Learning Engineer, Circuit Design - New College Grad 2026

NVIDIA Santa Clara, CA

Santa Clara, CA

7d ago

$116k - $219k/yr Full time Electrical Engineering Ai Assisted Engineering Artificial Intelligence

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Job Description

Applied Machine Learning Engineer, Circuit Design for New College Grad 2026 at NVIDIA in Santa Clara, CA (hybrid) with a salary range of USD 116,000 - 218,500 per year.

Responsibilities

Collaborate in a cross-functional team on programs spanning pre-silicon and post-silicon hardware design data, circuit optimization, SPICE correlation, and AI powered design automation.
Contribute to projects across silicon data analysis, manufacturing process variation analysis, VLSI circuit design and timing, and agent driven design exploration with optimized agent flows.
Translate stakeholder requirements into data science, AI/ML, and agent-based system problems; design architectures and implement end-to-end solutions.
Develop, validate, and release models and AI systems that integrate with existing machine learning, design automation, and visualization tools.
Analyze datasets, formulate and validate hypotheses, extract meaningful features, and build models plus self-improving workflows atop them.
Refine models, algorithms, and autonomous optimization loops until the desired QOR is achieved.

Requirements

Master’s degree or PhD in Electrical or Computer Engineering, Computer Science, or Applied Mathematics, or equivalent practical experience.
Strong background in circuit design, VLSI, ASIC, EDA, silicon analysis, or custom circuit design.
Demonstrated experience at the intersection of applied math, machine learning, and software development, with proficiency in Python and C++.

Technologies

Python
C++
PyTorch
LangChain
LangGraph
SPICE

Benefits

Equity
Benefits

Ways to Stand Out

Experience building AI systems for EDA, design automation, or circuit design workflows.
Research or project work in AI-driven EDA, circuit optimization, design-space exploration, or autonomous design systems.
Background creating agent-based systems, autonomous optimization loops, self-improving AI platforms, or production-scale AI/ML pipelines.
Proficiency with deep learning algorithms and AI agent frameworks; hands-on use of PyTorch, LangChain, or LangGraph is highly beneficial.

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7d ago

Analytics Engineer - AI Trainer

DataAnnotation Santa Clara, CA 🇺🇸 Remote

Santa Clara, CA

7d ago

$50 - $100/hr Contract Ai Ml Ai Trainer Ai Training

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Job Description

DataAnnotation is seeking an Analytics Engineer - AI Trainer to help train AI models by evaluating AI-generated quantitative work, solving technical problems, and providing feedback to improve AI reasoning. This contract role offers remote work and flexible scheduling, with the position anchored in Santa Clara, California.

Location: Santa Clara, CA (remote)

Salary: USD 50 - 100 per hour

Job Type: Contract

Experience required: 3+ years

Responsibilities

Assess AI-produced quantitative outputs across statistics, predictive modeling, scientific reasoning, and data-driven insights for technical accuracy and real-world validity.
Develop and solve quantitative tasks used to train and benchmark AI systems, covering areas such as forecasting, experimental analysis, optimization, and statistical inference.
Produce clear technical explanations and well-documented analytical code.
Deliver feedback that informs the next generation of AI models designed for quantitative reasoning.

Requirements

3+ years of hands-on experience in a quantitative role or research environment, such as data science, statistics, economics, finance, physics, biology, epidemiology, operations research, or related fields.
Some coding experience, with comfort writing and reviewing analytical code end-to-end.
Practical experience with statistical methods, predictive modeling, and experiment design (including A/B testing, hypothesis testing, regression, classification, and time-series forecasting).
Fluency in English with strong writing skills (native or bilingual level).
Bachelor’s degree in a quantitative field preferred; advanced degrees (Master’s or PhD) are a plus.
Credentials or demonstrated expertise (e.g., Kaggle rankings, AWS/GCP ML certifications) are a plus.

Benefits

Fully remote: work from anywhere in the US, Canada, UK, Ireland, Australia, and New Zealand.
Flexible schedule: select projects and set your own hours from your own computer at home.
Competitive pay: hourly rate of $50-$100, with potential bonus rates on some projects.
Impact: contribute to shaping AI systems that reason about data and analytics.

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9d ago

Senior Machine Learning Engineer

NVIDIA Santa Clara, CA

Santa Clara, CA

9d ago

$184k - $357k/yr Full time Senior Artificial Intelligence Autonomous Vehicles

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Job Description

Join NVIDIA in Santa Clara for an onsite Senior Machine Learning Engineer role that foregrounds what you gain: a competitive salary range of USD 184,000 to 356,500 per year, equity participation, and comprehensive benefits, all while collaborating across LiDAR and camera teams to advance autonomous driving. You will help build the ML backbone of the DRIVE AV perception stack, focusing on LiDAR and camera perception, data pipelines, and production-ready C++ code.

Responsibilities

Design, train, and optimize ML models for LiDAR perception, including road element detection, semantic segmentation, and tracking.
Build and coordinate end-to-end ML workflows, spanning data pipelines, model training, evaluation metrics, continuous performance instrumentation, and reporting.
Productization: transition models from initial evaluation to shipped components within the DRIVE AV platform, delivering efficient production-grade C++ code.
Innovation: stay current with ML advances and integrate techniques that boost platform performance.
Collaborate with LiDAR and camera teams, developers, engineers, and managers to turn complex ideas into reliable autonomous driving solutions.

Requirements

BS or MS in Computer Science, Engineering, or a related field, or equivalent experience.
6+ years of relevant proven industry experience applying machine learning to address real-world problems.
Strong C++ and Python programming and debugging skills with experience in developing for large, complex systems.
Deep practical experience applying ML to LiDAR/camera perception in automotive or related fields.
Experience with deep learning frameworks such as PyTorch or TensorFlow and a solid understanding of ML mathematics.
Building and sustaining training and essential metric workflows for large-scale datasets.
Excellent communication and analytical skills, with a self-motivated drive to solve hard problems.

Technologies

C++
Python
PyTorch
TensorFlow
TensorRT

Benefits

Equity
Benefits

Ways to Stand Out

LiDAR or Camera Perception Experience: Proven track record of developing and shipping deep learning models for LiDAR/Camera in a production environment.
Advanced Model Knowledge: Familiarity with modern network architectures like Transformers and their application to visual recognition tasks.
AV Production Experience: A history of delivering ML features and models into a production autonomous vehicle stack or a related robotics product.
Performance Optimization: Experience with model optimization for real-time inference on embedded or automotive platforms, such as TensorRT.

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10d ago

Principal Machine Learning Engineer, Accelerated Apache Spark

NVIDIA Santa Clara, CA

Santa Clara, CA

10d ago

$272k - $431k/yr Full time Python Big Data Bigdata

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Job Description

NVIDIA seeks a Principal Machine Learning Engineer to guide ML engineering on the GPU-accelerated Apache Spark program in Santa Clara, focusing on ML-driven performance prediction and optimization for Spark workloads. The role also involves building AI-based deployment tools and providing mentorship across environments. This onsite position offers a salary range of USD 272,000 to 431,250 per year.

Role overview

In this capacity, you will lead the ML engineering efforts for a GPU-accelerated Spark initiative, developing ML-based methods to forecast performance and optimize Spark workloads. You will additionally create AI-enabled tools and provide technical mentorship to enable deployment across diverse environments.

Responsibilities

Design and implement ML solutions for forecasting performance and optimizing GPU-accelerated enterprise Apache Spark workloads.
Develop advanced algorithms and adaptive systems to continually enhance Spark performance on GPUs.
Create AI-driven agents and tools to assist with diagnosing system issues and optimizing applications.
Collaborate with partners and customers to deploy complex ML solutions across varied environments.
Maintain deep domain expertise by staying current with the latest advances in ML systems and algorithms.
Provide technical leadership and mentorship in data science and ML for a team of engineers.

Requirements

Education: BS, MS, or PhD or equivalent experience in Machine Learning, Data Science, Computer Science, or a closely related field.
12+ years of professional experience designing, implementing, and productionizing high-quality ML/DL solutions.
5+ years of experience as a technical lead in ML model development.
Proven hands-on experience (2+ years) with large-scale data processing platforms such as Apache Spark.
Demonstrated ability to apply modern tooling and robust practices across all stages of building, deploying, and maintaining ML models.
Excellent programming skills in Python and related data science libraries (NumPy, pandas, scikit-learn, SciPy, PyTorch, TensorFlow).
Deep expertise in advanced ML methodologies, including LLMs/GenAI, reinforcement learning, and adaptive online ML systems.
Strong capability in feature engineering, feature importance assessment, and developing boosted tree models (e.g., XGBoost).

Technologies

Python
NumPy
pandas
scikit-learn
SciPy
PyTorch
TensorFlow
Apache Spark
XGBoost
Scala
Java
C++
CUDA

Benefits

Equity and benefits

Ways to stand out from the crowd

Understanding of the internal workings and architecture of Apache Spark
Familiarity with NVIDIA GPUs and CUDA
Experience coding in Scala, Java, and/or C++

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10d ago

Senior Machine Learning Engineer, End‑to‑End Autonomous Driving

NVIDIA Santa Clara, CA

Santa Clara, CA

10d ago

$184k - $357k/yr Full time Senior Active Learning Artificial Intelligence

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Job Description

NVIDIA in Santa Clara is seeking a Senior Machine Learning Engineer to design, train, and deploy end-to-end autonomous driving models while building data-centric pipelines and data flywheels that accelerate development. This onsite role involves close collaboration with researchers and engineers to convert advanced research into robust, production-ready ML systems for autonomous vehicles.

Responsibilities

Design, implement, and train scalable end-to-end driving models for autonomous vehicles.
Drive the data flywheel by identifying failure modes, defining data collection and labeling needs, and iterating models to close real-world performance gaps.
Build, curate, and maintain high-quality multimodal datasets including video, sensor streams, and language/action traces for end-to-end driving.
Develop and apply data-centric learning strategies such as active learning, curriculum learning, automated hard-example mining, outlier and novelty detection, and semi/self-supervised methods.
Explore and productize new data sources, including simulation, synthetic data, and world-model-based generation or augmentation to improve coverage and robustness.
Design and implement automated data workflows that handle discovery, labeling, evaluation, and retraining to maximize development velocity.
Foster collaborative partnerships with researchers and engineers to transform innovative research into robust, production-ready ML models.

Requirements

PhD with 4+ years, MS with 6+ years, or BS (or equivalent) with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related field.
Strong background in modern deep learning, including transformer architectures, video modeling, and multimodal VLM/VLA or foundation models.
Hands-on experience training and deploying deep learning models on real-world datasets, covering preprocessing, distributed training, evaluation, debugging, and iterative improvement.
Practical experience with data-centric methods such as active learning, curriculum learning, outlier/novelty detection, or large-scale sample mining.
Proficiency in Python and at least one major deep learning framework (PyTorch, TensorFlow, or JAX), plus solid software engineering practices (testing, code reviews, CI/CD).
Demonstrated ability to collaborate across teams, drive designs from prototype to production, and communicate with technical and non-technical partners.
Track record of leading complex cross-team projects, setting technical direction, and making critical technical decisions that impact multiple teams or products.

Technologies

Python
PyTorch
TensorFlow
JAX

Benefits

Equity
Benefits

Ways to stand out from the crowd

Experience building and operating data flywheels or large-scale data pipelines for ML, including data quality monitoring and continuous retraining loops.
Direct experience with end-to-end driving models, large-scale behavior cloning, or reinforcement/imitation learning for driving or robotics.
Experience leveraging simulation, synthetic data, or world models to generate training and evaluation data for autonomous systems.
Contributions to advanced data-centric ML methods, VLM/VLA, or autonomous driving through publications, open-source projects, or widely used internal tools.
Background with safety, reliability, and validation requirements for autonomous driving or other safety-critical applications.