Staff Software Engineer, Machine Learning - Personalization

DoorDash, Inc

Hybrid Actively hiring Posted today Verified listing
San Francisco, CA · Sunnyvale, CA Posted 1 day ago $137,100–$201,600 / year
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99+ other roles within 25 mi Near San Francisco, CA

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $204k
This role $169k
$122k most similar roles pay here $277k

This role pays less than 78% of similar roles. Most pay $177,325–$230,650 — the shaded band above. At the midpoint, this role pays about $169k versus about $204k for comparable roles.

Based on 240 similar postings.

Employer

About DoorDash, Inc

DoorDash, Inc. is an American company operating online food ordering and food delivery. It trades under the symbol DASH. With a 56% market share, DoorDash is the largest food delivery platform in the United States.

DoorDash, Inc currently has 238 open roles on FindRole.

Listed pay typically runs $131,600–$193,500 across 156 roles with salary data.

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At a glance

TL;DR

As a Staff Software Engineer in Machine Learning on DoorDash’s Personalization team, you will conceptualize and implement advanced machine learning solutions to enhance the personalized shopping experience across various retail categories. Your daily tasks include developing production-level ML models for growth and personalization, collaborating with cross-functional teams to shape product roadmaps, and mentoring junior engineers. Ideal candidates have over 8 years of industry experience in causal inference and recommendation systems, proficiency in Python, PyTorch or TensorFlow, and a strong background in applied machine learning. This role demands expertise in communicating technical insights effectively and driving impactful solutions within the fast-paced retail delivery sector.

Skills

Python PyTorch TensorFlow Causal Inference Recommendation Systems MAB Algorithms LLMs CI/CD

What you'll do

  • Develop production machine learning solutions for personalized shopping experiences.
  • Design and implement algorithmic improvements for growth and personalization models.
  • Mentor junior team members and lead cross-functional teams to enhance ML projects.
  • Collaborate with engineering and product leaders to shape the ML product roadmap.
  • Apply expertise in causal inference, recommendation systems, and deep learning.

What we're looking for

  • 8+ years of industry experience developing impactful machine learning models and shipping solutions to production.
  • M.S. or PhD in a quantitative field such as Statistics, Computer Science, Math, Operations Research, Physics, Economics.
  • Expertise in applied ML for causal inference and recommendation systems, including classical and deep learning methods.
  • Proficiency in Python with experience using PyTorch or TensorFlow for machine learning tasks.
  • Strong ability to communicate technical details effectively to non-technical stakeholders.
  • Demonstrated commitment to rigorous testing and data-driven growth mindset.

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