Synthetic Data

Synthetic Data Developer Jobs

3 jobs found

2d ago

Senior Test Data Management / Test Data Engineer/Test Data Architect

Capgemini Alpharetta, GA

Alpharetta, GA

2d ago

$33 - $52/hr Contract Senior Data Architecture Data Engineer

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Job Description

Capgemini is seeking a Senior Test Data Management / Test Data Engineer/Test Data Architect to lead end-to-end test data management for QA across SIT, UAT regression, and performance testing. The role focuses on designing, provisioning, refreshing, masking/anonymizing, and automating test data across multiple environments, with tight integration into CI/CD and DevOps workflows.

Key Responsibilities

Own an end-to-end test data management strategy for QA SIT, UAT regression, and performance testing.
Design, create, provision, refresh, and maintain test data across multiple environments.
Work with enterprise databases including Oracle, SQL Server, PostgreSQL, and DB2, plus other related platforms.
Develop SQL scripts and automation to create and validate test data.
Implement data masking, anonymization, and synthetic test data solutions to protect sensitive information.
Manage test data request dependencies, refresh cycles, and environment synchronization.
Integrate TDM processes with CI/CD and DevOps pipelines.
Collaborate with QA, development, DevOps, data engineering, and business teams.
Establish governance standards, audit controls, and reusable test data processes.
Troubleshoot data-related defects and perform root cause analysis.
Mentor junior TDM/QA engineers and provide technical leadership.

Required Skills

Strong SQL and database knowledge.
Test Data Management tools such as Delphix, Informatica Test Data Management, IBM InfoSphere Optim, or similar.
Experience with data masking and synthetic data generation.
ETL data integration concepts.
API testing and automation.
CI/CD tool familiarity such as Jenkins, Git, Azure DevOps, or similar.
Scripting experience using Python, Java, or Shell.
Cloud platform experience such as AWS and/or Azure.
Strong understanding of SDLC and Agile/Scrum.

Technologies

Oracle, SQL Server, PostgreSQL, DB2, Delphix, Informatica Test Data Management, IBM InfoSphere Optim, SQL, Python, Java, Shell, Jenkins, Git, Azure DevOps, AWS, Azure, CI/CD, DevOps, Agile/Scrum, ETL, API testing

Location and Employment Details

Location: Alpharetta, GA, United States (onsite)
Job Type: Contract
Salary: USD 33 - 52 per hourly

Benefits

Medical
Dental
Vision
Retirement benefits

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20d ago

Senior Synthetic Data Engineer - Autonomous Driving

NVIDIA Santa Clara, CA

Santa Clara, CA

20d ago

$184k - $357k/yr Full time Senior Autonomous Driving Simulation Cloud Data Engineering

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Job Description

NVIDIA is building the next generation of simulation and synthetic data workflows through NVIDIA DRIVE, with a focus on producing high-quality multi-sensor datasets for autonomous driving training. In this role, you will design and develop simulation environments and synthetic data pipelines using NVIDIA Omniverse NuRec and Cosmos, supporting both sensor simulation and dataset evaluation at scale. The work spans reconstruction-driven worlds, controllable scenario generation, and the engineering needed to run production-grade pipelines in data center or cloud environments.

What you’ll do

Build, implement, and optimize tools that generate synthetic data for training different DRIVE deep learning networks, covering simulated lidar, radar, camera/RGB-D, bounding boxes, object tracks, world models, segmentation, depth, scene semantics, and sensor metadata.
Develop lidar and radar sensor simulation workflows that operate on NuRec reconstructed driving worlds and Cosmos-generated environments, including sensor placement, calibration, material response, geometry handling, noise modeling, and scenario variation.
Develop a Cosmos world model to improve world generation, including controllable scenario generation, novel view synthesis, trajectory extrapolation, scene completion, quality triage, regression detection, and controllability evaluation.
Gather perception, planning, and DRIVE network requirements, then align them with current synthetic data and sensor simulation capabilities, creating new tools or improving existing performance when gaps appear.
Create dataset quality assessments and synthetic-real comparison procedures to evaluate sensor realism, annotation quality, distribution coverage, scenario diversity, and sim-to-real transfer for autonomous driving.
Set up, profile, and oversee large-scale NuRec, Cosmos, and sensor simulation pipelines in data center or cloud environments.
Debug cross-stack systems across sensors, reconstruction models, world models, simulation runtime, GPU workloads, distributed data services, and downstream autonomous-driving workloads.

Qualifications

B.S. or M.S. in Computer Science, Electrical Engineering, Computer Engineering, Applied Math, Physics, or a related field (or equivalent experience).
8+ years of experience in computer graphics, computer vision, autonomous driving, sensor simulation, neural rendering, or physically-based sensor modeling, synthetic data generation, or closely related software engineering roles.
Strong Python and C++ skills, with experience building, debugging, profiling, and maintaining production-quality systems on Linux.
Solid mathematical foundation in linear algebra, geometry, and probability.
Familiarity with synthetic data annotations, data formats, dataset curation, data augmentation, and evaluation workflows for perception model training and validation.
Familiarity with deep learning workflows and modern ML tooling, including the ability to translate network needs into synthetic data requirements and measurable quality criteria.
Experience with scalable engineering workflows such as Git, Docker, Kubernetes, CI/CD, distributed storage, and deployment in data centers or cloud environments.

Technologies

Python, C++, Linux
Git, Docker, Kubernetes, CI/CD
NuRec, Cosmos, NVIDIA Omniverse
Distributed storage, cloud, data center

Compensation and eligibility

Salary: USD 184,000 - 356,500 per year. You will also be eligible for equity and benefits.

Ways to stand out

Practical experience working directly with NVIDIA NuRec, Cosmos, world foundation models, Real2Sim systems, or autonomous-driving simulation and validation pipelines.
Experience in NuRec world reconstruction, neural rendering, 3D Gaussian Splatting, NeRFs, or occupancy networks.
Deep lidar or radar simulation expertise, including ray tracing or ray casting, reflectance and intensity modeling, Doppler, radar cross-section, weather effects, occlusion, and sensor-specific noise models.
Experience developing synthetic data pipelines for autonomous driving, including closed-loop simulation, domain randomization, long-tail scenario mining, or sim-to-real transfer.
Familiarity with autonomous vehicle data pipelines such as OpenDRIVE, HD maps, scenario formats, vehicle dynamics, or AV safety validation.

Location: Santa Clara, CA (onsite).

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2mo ago

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

NVIDIA Santa Clara, CA

Santa Clara, CA

2mo ago Closed

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

This position is no longer accepting applications (closed Aug 13, 2026).

Job Description

Join NVIDIA in Santa Clara, onsite, to lead the design, training, and deployment of end-to-end autonomous driving models. This senior role offers a salary range of USD 184,000 to 356,500 per year, plus equity and a comprehensive benefits package. You will collaborate across teams to build data-centric pipelines, drive data flywheels, and translate research into robust, production-grade machine learning systems for autonomous driving.

Benefits

Equity
Benefits

Responsibilities

Design, implement, and train large-scale end-to-end driving models.
Lead the data flywheel by identifying failure cases, specifying data collection and labeling needs, and iterating models to close real-world gaps in performance.
Build, curate, and maintain high-quality multimodal datasets (video, sensor, language/action traces) tailored for end-to-end autonomous driving.
Apply data-centric learning methods such as active learning, curriculum learning, automated hard-example mining, outlier and novelty detection, and semi/self-supervised approaches.
Explore and productize new data sources including simulation, synthetic data, and world-model based generation to improve coverage and robustness.
Design data workflows that automate data discovery, labeling, evaluation, and retraining to maximize development velocity.
Foster collaborative partnerships with researchers and engineers to transform innovative research into robust, industrial-strength models.

Requirements

PhD with 4+ years, MS with 6+ years, or BS (or equivalent experience) with 8+ years of relevant work in Computer Science, Computer Engineering, or a related technical field.
Strong background in modern deep learning, including transformer-based architectures, video modeling, and multimodal VLM/VLA or foundation models.
Hands-on experience training and deploying deep learning models on real-world datasets: data 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), along with solid software engineering practices (testing, code review, CI/CD).
Proven ability to collaborate across teams, drive designs from prototype to production, and communicate clearly with technical and non-technical partners.
A track record of leading complex cross-team projects, setting technical direction, and making critical decisions that impact multiple teams or products.

Technologies

Python
PyTorch
TensorFlow
JAX

Ways to stand out

Experience building and operating data flywheels or large-scale ML data pipelines, 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.

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Synthetic Data Developer Jobs

Senior Test Data Management / Test Data Engineer/Test Data Architect

Senior Synthetic Data Engineer - Autonomous Driving

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

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