Software Engineering Intern, Machine Learning Platform

Woven by Toyota
US - California - Palo Alto
January 19, 2025

Woven by Toyota is the mobility technology subsidiary of Toyota Motor Corporation.
Our mission is to deliver safe, intelligent, human-centered mobility for all.
Through our Arene mobility software platform, safety-first automated driving technology and Toyota Woven City - our test course for advanced mobility - we're bringing greater freedom, safety and happiness to people and society.
Our unique global culture weaves modern Silicon Valley innovation and time-tested Japanese quality craftsmanship.
We leverage these complementary strengths to amplify the capabilities of drivers, foster happiness, and elevate well-being.
TEAM
We work on the ML training and deployment ecosystem in AD/ADAS.
You will be embedded within the Automated and Assisted Driving Team, and alongside other teammates, work directly with Autonomy ML engineers in Perception and Planning to accelerate development and deployment of ML models.
Our mission is to provide scalable, reliable, and cost effective frameworks that enable fast delivery of high quality ML models, from data curation all the way to push button model deployment.
Who We Are Looking For
We are looking for a software intern who is passionate about large scale ML infrastructure systems, and is excited about improving reliability and speed of our ML development process by bringing state of the art insights from the broader ML community.
You are excited about leveraging your first-hand experience in training ML models toward identifying and improving impactful infrastructure components.
Your role would involve improving our dataset creation workflows, distributed training infrastructure, and efficiency of our metrics pipeline.
You would have the chance to impact the core infrastructure that is heavily used by all AD/ADAS ML engineers on a daily basis.
You will collaborate closely with one of our senior engineers, and receive feedback not only from other teammates, but also from ML engineers who will be using your product, so you can make it better along the way!
RESPONSIBILITIES
• Gain hands on experience with our production grade infrastructure components and identify the hot spots with the help of other team members
• Enhance observability of our infrastructure by augmenting training and evaluation pipeline with profilers and telemetry
• Engage with other team members to brainstorm about potential areas of improvement in our ecosystem
• Work collaboratively with other team members to integrate ML Ops tools into our ecosystem
• Enhance reliability of our infrastructure by devising thoughtful integrations tests
• Quantify improvements through rigorous benchmarking, and document your key findings
• Prepare 2 reports and continuously present your work to the team
MINIMUM QUALIFICATIONS
• Currently pursuing BSc, Masters, or PhD in Computer Science, Computer Engineering or similar disciplines
• Expert in Python and familiarity with PyTorch
• Experience with containerization systems, e.g.
Docker
• Experience building data processing workflows, e.g.
Kubenetes, Airflow, Flyte
• Evidence of developing software tools or contributing to open source software projects
• Experience with versioned control systems, e.g.
git
• Familiarity with benchmarking and A/B testing frameworks.
NICE TO HAVES
• Experience with distributed training frameworks
• Knowledge of cloud infrastructure, e.g.
AWS, GCP, Azure
• Continuously learning about recent developments in the ML Ops community, and bringing best practices in dataset curations, training ML models, and evaluating them to the team
• Experience working with ML models in the context of autonomous driving or robotic systems
• Familiarity with C++
• Excellent written and verbal communication skills
Our Commitment
• We are an equal opportunity employer and value diversity.
• Any information we receive from you will be used only in the hiring and onboarding process.
Please see our privacy notice for more details.

Read the full job description and apply online on the recuiter's web-site

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