Machine Learning Engineer

Your Job DarkVision is seeking a Machine Learning Engineer to join our Imaging & AI team. You will build and maintain the inference and training pipelines to support our machine learning workloads. You will help productionalize research code into robust, scalable software pipelines. DarkVision’s ultrasound imaging system collects huge datasets on the order of terabytes, detecting sub-millimetric defects in industrial assets that span hundreds of kilometers. Reliable and automated processing of this terabyte-scale data is crucial. As a Machine Learning Engineer, you will focus on the design, implementation, and maintenance of the software infrastructure that allows our models to train reliably and infer efficiently. This role is on-site at our North Vancouver, BC HQ, where employees enjoy a wide array of amenities including a fully equipped gym, squash court, steam room, climbing wall, and more! Our Team Working in the Imaging & AI team, you will join a multidisciplinary group of scientists and engineers. This team is responsible for early-stage ideation, research, experimentation, and development. You will collaborate closely with other technical members to ensure our models are integrated seamlessly into our products. What You Will Do • Inference Pipeline Engineering: Develop and maintain the code responsible for processing data through our models. You will ensure efficient execution of inference tasks, focusing on the optimal use of computational resources. • Training Infrastructure & Automatic Retraining: Design and implement automated pipelines for model training and retraining. You will build systems that allow for repeatable, scalable training loops. • Lifecycle Management: Establish and maintain best practices for model and dataset versioning. You will implement the tooling that tracks model lineage, connecting specific model versions to the exact data and hyperparameters used to create them. • Data Integration: Write the logic required to interface with internal data ingestion systems. You will handle the efficient loading, pre-processing, and movement of data to ensure pipelines are fed correctly. Who You Are (Basic Qualifications) • Bachelor’s degree in Computer Science, Engineering, or a related field. • 2+ years of experience in software engineering or machine learning engineering. • Professional (production-code) level proficiency in Python, with a focus on writing clean, modular, and tested code. • Ability to translate experimental code into production. • Experience with deep-learning frameworks, specifically PyTorch. • Understanding of high-performance computing, parallel processing, and distributed systems. What Will Put You Ahead • Experience with workflow orchestration tools (e.g., Prefect, Airflow, or Dagster). • Familiarity with distributed computing frameworks (e.g., Ray, Dask, Monarch). • Experience with MLOps tools for experiment tracking and artifact management (e.g., Weights & Biases, DVC, MLFlow). • Experience with model optimization and acceleration techniques, including TensorRT, ONNX, and CUDA. Familiarity with quantization and mixed precision training as well as profiling and debugging for latency and throughput. • Knowledge of PyTorch Lightning, Hugging Face Accelerate, or similar ML frameworks. • Working understanding of SQL and databases. • Strong communication skills to articulate engineering constraints to diverse technical teams. General Salary Range For this role, we anticipate paying $100,000 to $150,000 per year. This role is eligible for variable pay, issued as a monetary bonus or in another form. At Koch companies, we are entrepreneurs. This means we openly challenge the status quo, find new ways to create value and get rewarded for our individual contributions. Any compensation range provided for a role is an estimate determined by available market data. The actual amount may be higher or lower than the range provided considering each candidate's knowledge, skills, abilities, and geographic location. If you have questions, please speak to your recruiter about the flexibility and detail of our compensation philosophy.