Job Title: Machine Learning Engineer - Autonomous Flight
Voxelis is a startup founded with a vision to revolutionize aerial wildfire suppression. By transforming certified helicopter airframes into autonomous UAV helitankers, we aim to build the world's most advanced wildfire suppression system. We’re building a diverse team of talented engineers, developers, aviators, and makers who are driven by engineering solutions that mitigate the catastrophic effects of wildfires and clear the flightpath to a cleaner future.
Stage: Early-stage startup with ambitious goals and tight timelines.
Location: Onsite, YVR, Richmond, BC
Environment: Dynamic, fast-paced, collaborative, inclusive.
Duration: Initial 6-month contract with a clear end-objective. High potential for full-time extension post successful funding round.
Experience Levels: Open to diverse experience levels, from 4th-year co-ops to senior engineers. All applicants will be asked to demonstrate how their skills, experience and interests will help further our mission.
Start Date: 2nd Half October
As Machine Learning Engineer - Autonomous Flight, you'll build the foundation for harnessing and leveraging training data gathered by VoxVision This integrated sensor array, the first of its kind certified for flight on piloted civil helicopters in an operational setting, will be pivotal in collecting data for AI-driven autonomous flight in wildfire scenarios.
- Utilize in-flight data to construct a resilient and scalable learning-based autonomous helicopter flight system.
- Conduct distributed training of neural networks tailored for autonomous flight.
- Address challenges spanning multiple domains, such as high-level decision-making, interactions between multiple agents, agent modeling, and trajectory plotting.
- Establish a framework to support the life cycle of neural networks, anticipating the future growth of our data collection fleet.
- Apply machine learning, particularly deep learning, to visual recognition tasks like object identification, localization, semantic segmentation, and scene comprehension.
- Design and execute deep learning solutions to tackle real-time computer vision challenges for UAVs in wildfire contexts, including optical flow estimation, depth perception, object detection, tracking, and segmentation.
- Manage and refine datasets to optimize deep learning algorithms.
- Swiftly prototype and validate innovative machine learning, deep learning, and image processing techniques.
- Develop comprehensive machine learning solutions, enabling helicopters to detect wildfire targets, other aircraft, and more.
- Graduate (preferred) or Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering or related programs.
- Strong experience with Python and software engineering best practices
- Experience with Pytorch, TensorFlow, Keras, or MXNet or another major deep learning framework
- Detailed knowledge of deep learning: layer details, loss functions, optimization, etc.
- Proven expertise in deploying production ML models for self-driving/navigation, computer vision, or similar processing at scale.
- Experience with Imitation Learning, Reinforcement Learning (offline/off-policy), modern Neural Network architectures (e.g., GPT, Diffusion), or related techniques.
- Strong background in designing, building, and productionizing motion planning algorithms, state estimation algorithms, probabilistic modeling.
- Strong communication skills, both written and verbal.
- Satisfactory background check for facility access
Competitive salary and equity negotiable based on experience.
Job Types: Full-time, Fixed term contract
Contract length: 6 months
Salary: $6,000.00-$12,000.00 per month
- Casual dress
- Discounted or free food
- Flexible schedule
- On-site parking
- Stock options
Flexible Language Requirement:
Ability to commute/relocate:
- Richmond, BC V7B 1K3: reliably commute or plan to relocate before starting work (required)
- Please briefly describe a project you've led or been a key contributor to you believe is relevant to the posting.
- Bachelor's Degree (required)
- Machine learning: 2 years (required)
Work Location: In person
Application deadline: 2023-10-07
Expected start date: 2023-10-15