We are an innovative, Vancouver-based startup at the forefront of robotics, AI, and machine vision technologies. Backed by VC funding and recognized as a 2024 BCTIA Growth Award winner, 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 hiring a Machine Learning Engineer to build and improve the tools, infrastructure, and pipelines that power our AI model training. This role focuses on enhancing the synthetic data pipeline, automating training evaluations, and developing systems that connect data quality to model performance. You will work closely with technical artists, applied scientists, and simulation developers to deliver scalable, production-grade systems for robust AI deployment. 

Employee Value Proposition (EVP) 

Purpose 
Your work will determine how reliably and accurately our AI models perform in real-world industrial environments. By improving the data they learn from, you will directly influence the precision, speed, and robustness of robotic systems operating on factory floors. 

Growth 
You will develop deep technical skills across computer vision, machine learning, simulation, and experimentation. You will gain experience connecting training data to model outcomes, applying state-of-the-art techniques, and improving models through systematic iteration and analysis. 

Motivators 
You will take full ownership of the connection between synthetic data and model performance. Your ideas, experiments, and tools will define how we train production-grade AI models and how we scale to new use cases with confidence and reliability. 

Major Objectives 

Identify and Advance Opportunities in the Data Generation Pipeline 
You will identify opportunities to improve the synthetic data pipeline and deliver a meaningful enhancement that increases dataset quality, control, or scalability. Your contribution will reflect your ability to assess what’s missing and act decisively to improve it. 

Design, Test, and Validate New Data-Centric ML Techniques 
You will propose and implement new data-generation or augmentation techniques based on your own assessment of model bottlenecks, training patterns, or failure modes. You will validate effectiveness through structured training experiments and benchmark results against existing approaches. Your work will contribute a measurable advancement to how Apera trains accurate, generalizable AI models. 

Collaborate Across Disciplines to Deliver Data-Driven Improvements 
Partner with ML scientists and technical artists to translate visual intuition and ML needs into robust software. Identify gaps in configuration, data realism, and augmentation strategy that impact performance. Ultimately you will deliver generalized improvements to Apera’s vision model training process. 

Critical Subtasks 

• Design and implement tooling to configure and control synthetic data generation at scale.
• Design new data generation strategies that introduce variation, realism, or structure to improve model generalization.
• Run targeted training experiments to measure the impact of your data approaches and guide future improvements.
• Build internal tools that expose dataset properties, track changes, and help others reason about training inputs. 

Culture and Situation Fit 

At Apera AI, we value initiative, clarity, and technical depth. This role is for someone who sees data as a core design space in machine learning—not just an input but a lever to drive performance. You’ll thrive here if you take ownership of the problems you see, enjoy collaborating across disciplines, and are excited to push the boundaries of how real-world AI systems are trained and deployed.  

Required Qualifications 

• Degree in computer science, engineering, applied mathematics, or a related technical field, or equivalent industry experience building ML systems.
• Strong experience writing and maintaining production-quality code
• Strong proficiency in Python and experience with machine learning frameworks such as PyTorch or TensorFlow.
• Solid understanding of ML training workflows, including dataset preparation, model evaluation, and performance diagnostics.
• Experience with synthetic data generation, simulation tools, or 3D rendering environments such as Blender or Unity. 

Bonus Experience 

• Ability to design, execute, and interpret training experiments to evaluate the impact of data and augmentation strategies.
• Comfortable working in Linux-based development environments and with Docker-based workflows.
• Experience with domain randomization, synthetic-to-real transfer, or sim-to-real techniques in robotics or computer vision.
• Background in computer vision tasks such as object detection, segmentation, or 6-DoF pose estimation.
• Experience working with cloud-based ML infrastructure. 

Why Join Us?

People are our greatest strength. They are friendly, smart, and driven to build amazing products; we tackle challenges as a team, we are close-knit and scrappy. We also offer competitive total compensation, health benefits, and vacation. Our teams are motivated, talented, hardworking, and have an entrepreneurial spirit. We enjoy making large impact, solving challenging problems rooted in real-world physics using science, imagination, creativity, and persistence.

What do we offer?

1. A chance to make a difference. People are our greatest strength they are friendly, smart, and driven to build amazing products; we tackle challenges as a team, we are close-knit and scrappy. Our teams are motivated, talented, hardworking and have both an intrapreneurial and entrepreneurial spirit. We enjoy making a large impact, solving challenging problems rooted in real-world robotic vision optimization using science, imagination, creativity, and persistence.
   
   
2. Build your skills. Build your career. We don’t just promise opportunities. We back them with personalized development plans, annual learning budgets, regular individual and team upskilling, and time dedicated to innovation. Come for the mission, stay to master it.
   
   
3. Equity That Empowers: A Rare Opportunity to Own What You Help Build. At Apera AI, equity isn’t just a benefit—it’s a belief system grounded in fairness, unity, and shared success. Every Aperian receives a meaningful ownership stake from day one, because those creating value should share in the rewards. While traditional companies reserve equity for a privileged few, we extend it to all employees. We’ve already gained considerable traction in transforming how manufacturers use AI and robotics. Join a mission-driven team where your ideas matter, your work shapes industries, and your ownership grows with every win. This is your invitation to be part of something rare: a company that shares both purpose and upside.
4. Straightforward compensation. At Apera AI, we believe that transparency and fairness are key to building a thriving team. For this Machine Learning Engineer role, we offer a competitive base salary range of $100,000 to $130,000 CAD per year.

This range reflects the base salary for a highly qualified candidate. The final offer will depend on a range of factors including your unique skills, experience, contributions to team diversity, and the value you bring to our vision of transforming industrial automation with AI-powered vision systems.