Sr. Machine Learning Engineer

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

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.