Machine Learning Engineer

We are an innovative, Vancouver-based startup at the forefront of robotics, AI, and machine vision technologies. Backed by VC funding and we've been recognized with the 2025 Frost & Sullivan Technology Innovation Leadership Award, the AAM Supplier Excellence Innovation Award, and the 2024 BC Tech "Company of the Year – Growth", we are on a mission to redefine the future of AI-driven robotic vision systems.  Apera AI helps manufacturers make their factories more flexible and productive. Robots enhanced with Apera's software have 4D Vision – the ability to see and handle objects with human-like capability. Challenging applications such as bin picking, sorting, packaging, and assembly are now open to fast, precise, and reliable automation. Apera is led by an experienced team from high-growth companies focused on robotics, artificial intelligence, and advanced manufacturing.

Apera AI is building the future of robotics and AI right here in Vancouver.  
 
Let's End Robot Blindness 

Robots can do amazing things, if they can see. That's where you come in. 
We're Apera AI. Our breakthrough vision systems turn blind robots into intelligent, adaptable workers. But that transformation doesn't happen without people, especially the ones who teach robots how to learn from data and see the world clearly. 

We're looking for a Machine Learning Engineer - Synthetic Data & Training Pipeline to build the systems that connect data quality to model performance. Someone who thrives on experimentation, automation, and impact, turning messy data into intelligence that works on real factory floors. 

This is an engineering-first role, not pure research, not isolated modeling. You'll design scalable data pipelines, automate training, and make sure our models trained in simulation succeed in the real world. 

What You'll Actually Do 

• Build and scale synthetic data generation pipelines that feed our 4D vision models. 
• Design and automate ML workflows for data processing, training, and evaluation. 
• Experiment with new augmentation and domain adaptation techniques that boost model robustness. 
• Collaborate with applied scientists, simulation engineers, and artists to bring data-driven ideas to life. 
• Create internal tools for dataset tracking, experiment reproducibility, and training diagnostics. 
• Continuously improve - apply AI-driven analytics to optimize data, accelerate iteration, and strengthen model reliability. 

Why This is a Career Move (Not Just a Job) 

At Apera, we don't just build robots, we build the intelligence that lets them see, adapt, and perform. 

Here's what makes this role special: 

• It's not just data - it's trust. The pipelines you build decide how confidently manufacturers rely on automation. 
• You'll grow fast - at the intersection of ML, simulation, and real-world robotics. 
• You'll see your work in action - every factory floor running Apera Vision runs on your models. 
• You'll shape how AI learns - designing systems that make synthetic-to-real training faster, smarter, and more scalable. 

What We're Looking For 

• 3+ years of experience in machine learning, computer vision, or data pipeline development 
• Proficiency with Python and ML frameworks (PyTorch, TensorFlow, or similar) 
• Experience building or maintaining synthetic data or simulation-based training pipelines 
• Strong understanding of dataset design, augmentation, and experiment tracking 
• Hands-on experience with CI/CD, cloud ML tools, or data orchestration frameworks 

Bonus points if you have: 

• Experience with domain adaptation, data-centric AI, or 3D rendering tool. 
• Familiarity with robotics, simulation environments, or digital twins 

Why Apera? 

• Competitive salary in the range of $100,000 - $130,000 CAD, plus equity and comprehensive benefits. 
• Equity for every employee - you share in what we build 
• Comprehensive health, dental, and vision coverage  
• A chance to make a difference.

At Apera AI, every employee owns equity, because those who build the future should share in its upside. Join us to make robotics more accessible, predictable, and powerful.