Senior ML Engineer — End-to-End Production AI (Hybrid)

DoorDash, Inc.; San Francisco, New York, Seattle (CA, NY, WA); Ibrido; USD 168.000 - 247.000; Tempo pieno

Vantaggi offerti da questo lavoro

Equity grants

401(k) plan with employer matching

Parental leave

Wellness benefits

Paid time off

Medical, dental, and vision benefits

Descrizione del lavoro

DoorDash, Inc. is seeking a Senior Software Engineer, Machine Learning to drive ML improvements for the catalog system and product knowledge graph. You will own projects end-to-end, mentor junior engineers, and collaborate with multi-disciplinary teams in a hybrid environment reporting to an Engineering Manager.

You will develop scalable ML models, manage offline and online phases, and deploy solutions with ongoing monitoring and measurement across the business.

Competenze

• MS or PhD in a technical field such as CS, mathematics, statistics, physics or equivalent.
• 5+ years of ML industry experience with solid understanding of ML algorithms and fundamentals.
• Experience building data/feature engineering pipelines at scale using PySpark and Snowflake SQL.

Mansioni

• Own end-to-end ML lifecycle: ideation, offline training, online shadowing/deployment, experimentation, monitoring.
• Design and implement advanced ML models to improve core catalog system and product knowledge graph.
• Mentor junior and mid-level engineers and collaborate with cross-functional teams.

Conoscenze

Machine learning algorithms

End-to-end ML lifecycle

Collaborative work

Formazione

MS or PhD in computer science / math / stats / physics or equivalent

Strumenti

PyTorch

Keras

LightGBM

scikit-learn

Spark ML

PySpark

Snowflake SQL

Descrizione del lavoro

DoorDash, Inc. is seeking a Senior Software Engineer, Machine Learning to drive ML improvements for the catalog system and product knowledge graph. You will own projects end-to-end, mentor junior engineers, and collaborate with multi-disciplinary teams in a hybrid environment reporting to an Engineering Manager.

You will develop scalable ML models, manage offline and online phases, and deploy solutions with ongoing monitoring and measurement across the business.