Principal Platform Software Engineer

Disclaimer:

Certain U.S. based or U.S. customer or client-facing roles may be required to comply with applicable requirements, such as immunization/occupational health mandates, and/or drug testing requirements.

Range and benefit information provided in this posting are specific to the stated locations only

US: Hiring Range in USD from: $104,500 to $234,600 per annum. May be eligible for bonus, equity, and compensation deferral.


Oracle maintains broad salary ranges for its roles in order to account for variations in knowledge, skills, experience, market conditions and locations, as well as reflect Oracle's differing products, industries and lines of business.
Candidates are typically placed into the range based on the preceding factors as well as internal peer equity.

Oracle US offers a comprehensive benefits package which includes the following:
1. Medical, dental, and vision insurance, including expert medical opinion
2. Short term disability and long term disability
3. Life insurance and AD&D
4. Supplemental life insurance (Employee/Spouse/Child)
5. Health care and dependent care Flexible Spending Accounts
6. Pre-tax commuter and parking benefits
7. 401(k) Savings and Investment Plan with company match
8. Paid time off: Flexible Vacation is provided to all eligible employees assigned to a salaried (non-overtime eligible) position. Accrued Vacation is provided to all other employees eligible for vacation benefits. For employees working at least 35 hours per week, the vacation accrual rate is 13 days annually for the first three years of employment and 18 days annually for subsequent years of employment. Vacation accrual is prorated for employees working between 20 and 34 hours per week. Employees working fewer than 20 hours per week are not eligible for vacation.
9. 11 paid holidays
10. Paid sick leave: 72 hours of paid sick leave upon date of hire. Refreshes each calendar year. Unused balance will carry over each year up to a maximum cap of 112 hours.
11. Paid parental leave
12. Adoption assistance
13. Employee Stock Purchase Plan
14. Financial planning and group legal
15. Voluntary benefits including auto, homeowner and pet insurance

The role will generally accept applications for at least three calendar days from the posting date or as long as the job remains posted.

Career Level - IC4

What you will do

• Select and adapt models and training approaches based on robotic tasks, available data, compute budgets, observation and action representations, and deployment constraints.
• Develop SFT and imitation-learning workflows to adapt pretrained robot models and learn policies from demonstrations. Diagnose optimization failures, overfitting, and weak generalization.
• Design RL environments and rewards in Isaac Sim, using Isaac Lab or comparable learning frameworks. Define observations, actions, resets, termination conditions, controllers, and curricula.
• Train, tune, and debug RL policies, investigating unstable learning, unintended reward exploitation, and simulation or controller mismatches.
• Prepare synchronized, compatible demonstration datasets. Validate sensor, state, action, timing, and normalization conventions; version training, validation, and evaluation splits and prevent leakage.
• Lead baseline comparisons, ablations, and held-out evaluation. Measure task success, robustness, and generalization; establish model acceptance criteria and distinguish simulation results from hardware evidence.
• Build reproducible GPU training and rollout workflows, tracking data, configurations, seeds, checkpoints, metrics, and recovery. Improve training throughput and resource use.
• Use operational data to guide simulation calibration, domain randomization, and retraining. Partner with robotics engineers on inference performance, observation/action compatibility, and supervised hardware validation.

• Lead architecture and technical reviews, resolve cross-team dependencies, mentor engineers, and deliver tested workflows. Validate AI-assisted code and experimental conclusions.

Required qualifications

• A bachelor's degree in Robotics, Computer Science, Mechanical Engineering, Electrical Engineering, or a related discipline, or equivalent practical experience.
• At least six years of experience in machine learning engineering, software development, or applied robotics research, including substantial hands-on robot learning.
• Hands-on experience training, fine-tuning, and evaluating robot policies using RL, SFT, and imitation learning, with measured task-level results.
• Experience with robot foundation models, vision-language-action models, or other learned control policies. Examples include GR00T, ACT, and diffusion policies; comparable experience is equally relevant.
• Practical understanding of RL environment and reward design, policy optimization, and learning diagnostics; ability to adapt pretrained models and train policies from demonstrations.
• Strong Python and PyTorch skills, including training-loop implementation, GPU profiling, and debugging on Linux.
• Hands-on development experience with NVIDIA Isaac Sim, including robot and scene configuration, physics, sensors, and automated experiments.
• Experience curating compatible robot datasets, preventing leakage, designing reproducible experiments, and evaluating generalization and robot behavior.
• Robotics fundamentals covering kinematics, dynamics, coordinate transforms, feedback control, and the effect of action representations, timing, and latency on learned behavior.

• A record of selecting models and training methods under task, data, compute, and deployment constraints, owning delivery across components, and mentoring engineers.

Preferred qualifications

• Experience transferring learned policies to physical robots and diagnosing simulation-to-reality differences.
• Experience with Isaac Lab, LeRobot, Gazebo, MuJoCo, or Nav2; synthetic data generation, 3D reconstruction, or domain randomization.
• Experience optimizing robot-model inference with TensorRT or comparable tools under latency and memory constraints.
• Experience with ROS 2, robot SDKs, URDF or USD assets, and model execution on robot or edge computers.
• Experience scaling training across GPUs or contributing to robot-learning libraries.
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Why join us

Turn robot data and rigorous experiments into policies that improve measurable robot behavior