Staff ML Engineer - Generative AI & On-Device AR

Snap Inc.; Los Angeles (CA); On-site; USD 229,000 - 343,000; Full time

Benefits offered by this job

Paid parental leave

Comprehensive medical coverage

Emotional and mental health support

Job summary

Snap Inc. in Los Angeles is seeking a Machine Learning Engineer to develop innovative ML technology powering millions of Snapchatters. You will build AR experiences using generative models and deliver on‑device inference.

You will collaborate with cross‑functional teams, stay updated with research, and apply deep learning techniques to vision and generative modeling. A strong ML background and experience with TensorFlow, PyTorch, or JAX are preferred.

Qualifications

• Bachelor’s degree in a technical field or equivalent experience.
• 8+ years of post‑Bachelor’s ML or related experience; or Master’s with 7+ years; or PhD with 4+ years.
• Experience with Computer Vision or Generative Modeling techniques.
• Experience with ML frameworks such as TensorFlow, PyTorch, JAX, MLX, scikit‑learn.

Responsibilities

• Develop innovative machine learning technology and products that serve millions of Snapchatters.
• Build cutting‑edge AR experiences using generative models.
• Deliver generative ML experiences on device.
• Partner with cross‑functional Snap teams to explore and prototype new products.

Skills

ML research

Neural networks

Deep learning

Generative modeling

Mathematics

Collaboration

Independent work

Education

Bachelor’s degree in CS/Math/Stats or equivalent

8+ years ML experience or 7+ with Masters or 4+ with PhD

Tools

TensorFlow

PyTorch

JAX

scikit-learn

Job description

Snap Inc. in Los Angeles is seeking a Machine Learning Engineer to develop innovative ML technology powering millions of Snapchatters. You will build AR experiences using generative models and deliver on‑device inference.

You will collaborate with cross‑functional teams, stay updated with research, and apply deep learning techniques to vision and generative modeling. A strong ML background and experience with TensorFlow, PyTorch, or JAX are preferred.