Senior ML Engineer — Scalable ML in Production

Snap Inc.; New York (NY); On-site; USD 173,000 - 259,000; Full time

Benefits offered by this job

Equity (RSUs)

Comprehensive medical coverage

Paid parental leave

Job summary

Snap Inc. is seeking a skilled ML Engineer to build and deploy models powering Snapchat’s core products. You will own data analysis to production deployment, partnering with engineering and product teams to ship ML‑driven features at scale.

You’ll leverage TensorFlow/PyTorch and modern ML techniques, focusing on performance, security, and production‑ready quality code in a fast‑paced environment.

Qualifications

• Bachelor’s Degree in a relevant technical field such as computer science or equivalent years of practical work experience
• 3+ years of post‑Bachelor’s machine learning experience; or Master’s degree in a technical field + 2+ years of post‑grad machine learning experience; or PhD in a relevant technical field
• Experience developing machine learning models for ranking, recommendations, search, content understanding, image generation, or other relevant applications of machine learning

Responsibilities

• Build and deploy machine learning models that power core products, serving millions of Snapchatters
• Apply modern ML techniques to solve large‑scale, real‑world problems
• Own the full ML lifecycle from data analysis to production deployment
• Partner with cross‑functional teams to prototype and launch ML‑driven features
• Utilize AI tools and high‑velocity engineering workflows to design and ship scalable services while upholding rigorous standards for code correctness, security, and production‑ready quality code

Skills

Machine learning

Data analysis

Collaboration

Ambiguity solving

Mentorship

AI tooling

Education

Bachelor's degree in CS or equivalent

MS/PhD beneficial

Tools

TensorFlow

PyTorch

Spark ML

scikit-learn

Job description

Snap Inc. is seeking a skilled ML Engineer to build and deploy models powering Snapchat’s core products. You will own data analysis to production deployment, partnering with engineering and product teams to ship ML‑driven features at scale.

You’ll leverage TensorFlow/PyTorch and modern ML techniques, focusing on performance, security, and production‑ready quality code in a fast‑paced environment.