Sr. Machine Learning Engineer

Parallel Domain; Vancouver; Hybrid; CAD 150,000 - 180,000; Full time

Job summary

An innovative firm is seeking a talented Sr. Machine Learning Engineer to join their dynamic team in Vancouver. In this role, you'll work on cutting-edge technology, developing perception algorithms and data pipelines that enhance the capabilities of autonomous systems. Collaborating with experts from various industries, you'll contribute to a platform that transforms AI development through synthetic data. This position offers a unique opportunity to grow in a creative, inclusive environment while tackling exciting challenges in the field of artificial intelligence. If you're passionate about pushing the boundaries of technology, this role is perfect for you.

Benefits

Employer-paid medical benefits

Flexible paid vacation

Paid parental leave

Equipment budget

Annual learning stipend

Qualifications

• Master's or PhD in computer science or related field required.
• Extensive experience in developing machine learning models.
• Ability to write clean and maintainable code.

Responsibilities

• Develop perception algorithms and data pipelines for synthetic data.
• Maintain and optimize codebase for fast experimentation.
• Summarize findings and support customers effectively.

Skills

Python

Machine Learning

Computer Vision

Data Pipelines

Communication

Education

Master’s in Computer Science

PhD in Computer Science

Tools

Pytorch

Tensorflow

Argo

Kubeflow

Airflow

Ray.io

Job description

Join to apply for the Sr. Machine Learning Engineer role at Parallel Domain

Join to apply for the Sr. Machine Learning Engineer role at Parallel Domain

At Parallel Domain, we believe that autonomous systems – from self-driving cars to contactless delivery drones – have the opportunity to drastically improve the quality of life for billions of people. That is why we love what we do: enabling our customers to develop their technology safely using the Parallel Domain data generation platform. Our platform revolutionizes the way our customers generate sensor data for AI development. We're looking for people like you to join us in pushing the boundaries of artificial intelligence and synthetic data.

In this role you will:

• You’ll be part of the ML research team tasked with establishing best practices on creating and using synthetic data for perception
• You’ll develop perception algorithms, data pipelines, introspection tools, domain gap measures and domain adaptation approaches
• You’ll summarize your findings to help guide our team as well as our customers
• You’ll sync with your team on a daily basis to discuss progress, ideas and problems
• You’ll work with veterans from the gaming, movie and automotive industry
  
  
  

What You’ll Own:

• Creating specific datasets for your experiments
• Adapting/implementing cutting-edge models and data pipelines
• Maintaining and optimizing our code base to enable fast experimentation
• Writing tools to inspect and measure the impact of synthetic data on training
• Summarizing and sharing your findings
• Supporting our customers
• Staying up to date with the latest research on synthetic data
  
  
  

About You:

• You're eager to learn
• You are a team-player and understand the importance of clear communication
• You thrive in ambiguous environmentsYou are not afraid to approach new challenges
• Master’s or PhD in computer science or a related field
• Well-versed with developing machine learning models for computer vision tasks in either an industry or research environment
• Extensive experience in python
• Ability to write clean/maintainable code
• Working knowledge of Pytorch or Tensorflow
• You are fluent in english
  
  
  

Bonus Points:

• Experience with developing, deploying and improving perception stacks on AVs
• Experience with domain adaptation approaches or custom domain gap measures
• Experience at a fast-growing, young company
• Experience with workflow management systems such as Argo, Kubeflow, Airflow, Ray.io
  
  
  

$150,000 - $180,000 a year

Commensurate with experience and performance.

Location Information: Most of our team works in a hybrid style in Vancouver, British Columbia. But we are also open to fully office-based employees or remote employees who can commit to working our core hours (Pacific 10 am to 4 pm) and travelling to our Vancouver office quarterly. In addition, we offer relocation support if candidates are open and interested in moving to Vancouver.

Why join Parallel Domain?

We are assembling a team of creative, talented visionaries seeking to build a new technology that will change the world. You will be able to learn, build, and scale our team and technology in a collaborative, creative culture that values every team member.

To attract and inspire the right talent, we offer:

• Competitive compensation
• Employer-paid supplemental medical, mental health, dental, and vision benefits.
• Flexible paid vacation, sick time, winter shutdown, and 11+ holidays per year
• Paid parental leave
• Equipment budget to optimize your setup
• Annual learning and development stipend
  
  
  

Parallel Domain celebrates diversity and is committed to creating a safe and inclusive environment for all our people. We are committed to providing employees with an environment free of discrimination, bullying and harassment. All employment decisions at Parallel Domain are based on business needs, job requirements and individual qualifications. We will maintain our commitment to and support of equal employment opportunity for all individuals without regard to race, national/ethnic origin, colour, religion, age, sex (including pregnancy), sexual orientation, gender identity/expression, marital status, family status, genetic characteristics or physical/mental disability. Our commitment extends to any other protected classes which may exist under applicable law.

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

• Industries

  Software Development

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