Machine Learning Engineer - ML Agents and Planning

Zoox
Foster City, CA
On-site

Who this role is best for

Best suited to mid-level machine learning engineers with expertise in reinforcement learning and transformer models, working in autonomous vehicle planning and prediction.

Best fit for

  • PhD or experienced master's holders with RL-based planning expertise.
    — “PhD degree in computer science or related field or master's degree and 5+ years of professional experience
  • Engineers comfortable with production ML pipelines and transformer architectures.
    — “Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines
  • Candidates who can bridge ML models with real-world driving metrics.
    — “develop metrics and tools to analyze errors and understand improvements of our systems

Things to consider

  • Compensation includes RSUs and stock appreciation rights, not just salary.
    — “three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights
  • Location-specific salary adjustments apply within the listed range.
    — “Compensation will vary based on geographic location and level

How to stand out

  • Highlight publications in top-tier conferences like NeurIPS or CVPR.
    — “Top tier publications (NeurIPS, ICML, CVPR)
  • Demonstrate cross-functional collaboration with perception and planning teams.
    — “collaborate with engineers on Perception, Planning,Simulation, and Validation
  • Showcase experience in safety and comfort metrics for autonomous systems.
    — “estimate the quality of those driving plans along the dimensions of safety, progress, comfort and realism
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · TeamLevel · Senior

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • Develop new deep learning models
  • Work on novel techniques to estimate the quality of driving plans
Typical background
PhD degree in computer science or related field or master's degree and 5+ years of professional experience

Skills & requirements

Required

Deep LearningReinforcement LearningTransformer-based ModelsProduction ML Pipelines

Preferred

Top Tier Publications (neurips, Icml, Cvpr)

Stack & domain

PythonC++Reinforcement LearningTransformer-based ModelsMachine Learning PipelinesCollaborationProblem-solvingAutonomous DrivingMachine Learning

About the role

Original posting from Zoox via Lever

The Offline Driving Intelligence team is responsible for developing Foundation Models for ML Agents and planning, applying them off-vehicle to provide generalization capabilities to simulation and validation. Our team collaborates closely with the Planner, Simulation and Validation teams to develop and validate our driving performance. As an ML Agents and Planning Machine Learning Engineer you will work on the bleeding edge of the industry, developing novel machine learning pipelines and models to predict the behavior of other agents in the world and planning the best course of action for the ego vehicle.

In this role, you will...:

  • You will develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for human-like agents.
  • You will work on novel techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort and realism.
  • You will contribute to our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field
  • You will develop metrics and tools to analyze errors and understand improvements of our systems
  • You will collaborate with engineers on Perception, Planning,Simulation, and Validation to solve the overall Autonomous Driving problem.

Qualifications:

  • PhD degree in computer science or related field or master's degree and 5+ years of professional experience in a relevant field.
  • Experience in Planning and / or Prediction using Reinforcement Learning techniques 
  • Experience with training and deploying transformer-based model architectures
  • Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines
  • Fluency in Python with a basic understanding of C++

Bonus Qualifications:

  • Top tier publications (NeurIPS, ICML, CVPR)

There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.

Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.

Source: Zoox careers (Lever)

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