Simulation & Modeling

Simulation & Modeling Data Jobs

2 jobs found

1mo ago

Senior Synthetic Data Engineer - Autonomous Driving

NVIDIA Santa Clara, CA

Santa Clara, CA

1mo ago Closed

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

This position is no longer accepting applications (closed Oct 22, 2026).

Job Description

Senior Synthetic Data Engineer will design and build simulation environments and synthetic data pipelines for autonomous driving systems using NVIDIA DRIVE, NVIDIA Omniverse NuRec, and Cosmos.

Responsibilities

Build, implement, and optimize tools to generate synthetic data for training multiple DRIVE deep learning networks, including 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, covering sensor placement, calibration, material response, geometry handling, noise modeling, and scenario variation.
Create 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, map them to existing synthetic data and sensor simulation capabilities, and create or improve tools when gaps are identified.
Develop dataset quality assessment methods and synthetic-real comparison procedures to evaluate sensor realism, annotation quality, distribution coverage, scenario diversity, and sim-to-real transfer.
Set up, profile, and supervise large-scale NuRec, Cosmos, and sensor simulation pipelines in data center or cloud environments.
Debug end-to-end systems across sensors, reconstruction models, world models, simulation runtime, GPU workloads, distributed data services, and downstream autonomous-driving workloads.

Requirements

B.S. or M.S. in Computer Science, Electrical Engineering, Computer Engineering, Applied Math, Physics, or 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; experience building, debugging, profiling, and maintaining production-quality systems on Linux.
Strong 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, with enough practical understanding to translate network needs into synthetic data requirements and measurable quality criteria.
Experience with scalable engineering workflows including Git, Docker, Kubernetes, CI/CD, distributed storage, and deployment in data centers or cloud.

Technologies

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

Location and Salary

Location: Santa Clara, CA (onsite)
Compensation: USD 184,000 - 356,500 per year

Benefits

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 with NuRec world reconstruction, neural rendering, 3D Gaussian Splatting, NeRFs, or occupancy networks.
Deep lidar or radar simulation expertise: 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, and sim-to-real transfer.
Familiarity with autonomous vehicle data pipelines and formats such as OpenDRIVE, HD maps, scenario formats, vehicle dynamics, or AV safety validation.

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1mo ago

Machine Learning Engineer

Molex Austin, TX

Austin, TX

1mo ago Closed

$170k - $250k/yr Full time Python Artificial Intelligence Azure Gpu Compute

This position is no longer accepting applications (closed Sep 27, 2026).

Job Description

Molex is hiring a Machine Learning Engineer in Austin, TX (onsite) to help reduce reliance on full high-fidelity engineering simulations. This role focuses on building and operationalizing physics-informed surrogate models on Azure Machine Learning, so design teams can predict simulation outcomes from design parameters faster, with uncertainty-aware decisions about when full validation is still needed. The salary range is USD 170,000 - 250,000 per year.

What you’ll build and improve

Design and train surrogate models using neural networks, Gaussian processes, gradient-boosted trees, and GNNs/PINNs on Azure GPU compute (ND/NC series).
Apply physics-informed constraints so model outputs remain physically valid, not just statistically accurate.
Create model uncertainty and confidence scoring to identify which designs require full simulation validation.
Continuously retrain models as new simulation results arrive, improving performance over time.

Deploy, monitor, and benchmark

Deploy and version models using Azure ML endpoints and the model registry.
Monitor models on a rolling basis for drift.
Benchmark surrogate performance against full simulation to support platform-level performance tuning.

What you bring

Extensive hands-on production experience building, training, and deploying ML models (not limited to pretrained API usage).
10+ years building ML for physical or engineering systems, including surrogate modeling, physics-informed ML, or scientific ML.
Strong Python skills with PyTorch or TensorFlow.
Understanding of relevant engineering/physics fundamentals and simulation data formats for your domain.
Experience with Azure Machine Learning or a similar cloud ML platform.
Familiarity with uncertainty quantification, including Bayesian approaches and ensembling.

Additional advantages

Direct experience with industry-standard EM or physics simulation tools.
Background in geometric deep learning (graph neural networks and mesh-based models) for CAD data.
Experience in RF/high-speed electronics or interconnect design.

Benefits

Medical, dental, and vision
Flexible spending and health savings accounts
Life insurance
ADD, disability
Retirement
Paid vacation/time off
Educational assistance
May also include infertility assistance
Paid parental leave and adoption assistance

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Simulation & Modeling Data Jobs

Senior Synthetic Data Engineer - Autonomous Driving

Machine Learning Engineer

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