Job description

Duration: 12 months Responsibilities: • Run and iterate on camera/sensor simulation pipelines to generate synthetic datasets on demand • Deliver training/evaluation data to camera architects and ML teams under tight timelines • Support ML algorithm training and evaluation using the generated synthetic data — run training jobs, produce evaluation metrics, and iterate on dataset composition based on model performance • Configure simulation scenes and sensor parameters to match customer/architect specifications • Validate and quality-check generated data before hand-off • Maintain and improve simulation tooling and dataset generation scripts Minimum Qualifications • Bachelor's degree in Computer Science or a related field. • Minimum 5 years of experience in software engineering, with a focus on ML infrastructure design. • 5 years experience in Python and C. • Camera & sensor simulation fundamentals: • Camera intrinsic/extrinsic setup and calibration • Sensor modeling: noise sources, conversion gain, quantum efficiency, read/shot noise • Basic understanding of ISP and image formation • ML training & evaluation • Experience running ML training/evaluation workflows (PyTorch preferred) • Able to interpret model metrics and translate them back into dataset changes • Data handling: comfortable with large-scale dataset generation, storage, and versioning • Familiarity with Linux dev environments and source control • Experience building ML models and pipelines • Understanding how camera simulations work • Previous AI experience to develop ML architectures Preferred Qualifications • Experience with rendering engines (Blender, Unreal, Unity) or in-house simulation frameworks • Optical/imaging background (radiometry, PSF, MTF) • Prior experience with camera/ISP tuning or perception model development Pursuant to the California Fair Chance Act, Los Angeles County Fair Chance Ordinance for Employers, Los Angeles Fair Chance Initiative for Hiring Ordinance, and San Francisco Fair Chance Ordinance, qualified applicants will be considered for assignment with arrest and conviction records. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties 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, meet client expectations, standards, and accompanying requirements, and safeguard business operations and company reputation. About US Tech Solutions: US Tech Solutions is a global staff augmentation firm providing a wide range of talent on-demand and total workforce solutions. To know more about US Tech Solutions, please visit www.ustechsolutions.com . US Tech Solutions is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. AI Statement: By applying, you acknowledge that AI-assisted tools may be used during hiring. #LI-AS140

Requirements

Must-haves

Duration: 12 months
Run and iterate on camera/sensor simulation pipelines to generate synthetic datasets on demand
Deliver training/evaluation data to camera architects and ML teams under tight timelines
Support ML algorithm training and evaluation using the generated synthetic data — run training jobs, produce evaluation metrics, and iterate on dataset composition based on model performance
Configure simulation scenes and sensor parameters to match customer/architect specifications
Validate and quality-check generated data before hand-off
Maintain and improve simulation tooling and dataset generation scripts
Bachelor's degree in Computer Science or a related field.
Minimum 5 years of experience in software engineering, with a focus on ML infrastructure design.
5 years experience in Python and C.
Camera intrinsic/extrinsic setup and calibration
Sensor modeling: noise sources, conversion gain, quantum efficiency, read/shot noise
Basic understanding of ISP and image formation
ML training & evaluation
Able to interpret model metrics and translate them back into dataset changes
Data handling: comfortable with large-scale dataset generation, storage, and versioning
Familiarity with Linux dev environments and source control
Experience building ML models and pipelines
Understanding how camera simulations work

Nice-to-haves

Experience running ML training/evaluation workflows (PyTorch preferred)
Experience with rendering engines (Blender, Unreal, Unity) or in-house simulation frameworks
Optical/imaging background (radiometry, PSF, MTF)
Prior experience with camera/ISP tuning or perception model development
AI Statement: By applying, you acknowledge that AI-assisted tools may be used during hiring.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlpythonai

Platform: jobdiva
Effort: medium