ML Infrastructure Engineer

Overview

Build and maintain AWS cloud infrastructure for ML research and production workloads.

Key Responsibilities

• model optimization
• cloud infrastructure
• ci/cd automation
• ml ops
• collaboration

Tasks

You will be a part of the Machine Learning Team and work closely with Machine Learning DevOps Engineers, Machine Learning Scientists, Data Scientists, and Software Engineers. -Optimize ML models and classical algorithms to run efficiently and robustly -Develop and maintain cloud infrastructure to support research and production batch processing workloads -Build automation, CI/CD, and release management -Collaborate with software engineers for continuous improvement of end-to-end products from hardware tool to customer -Introduce and promote ML Ops best practices to accelerate research using modern tooling such as DVC, WandB, etc.

Requirements

• aws
• kubernetes
• python
• mlops
• bachelor's degree
• problem solving

What You Bring

DarkVision is seeking a ML Infrastructure Engineer, you will build and maintain cloud infrastructure to support cutting edge machine learning and signal processing research as well as production workloads at scale. You will proactively enhance the tools and processes to achieve excellence in reliability, efficiency, observability and security. You will promote DevOps and MLOps best practices to accelerate research and development. And you will spearhead cross-functional projects and serve as the bridge between software engineering, ML research, and data science. Successful candidates will be required to complete a criminal background check. keywords: ML, machine learning, infrastructure, cloud, AWS, CI/CD, build automation, Python, MLOps, DevOps, System Design, Kubernetes, Linux, Shell Scripting, data science, databases, data pipeline, computer vision, ultrasound, signal processing, -Knowledge of System Design, common architectural patterns and trade-offs. Particularly cloud-native development in the context of data-intensive, large-scale ML workloads -Having shipped at least one complex software product and supported it in production for real customers -Data Science skills (Prefect, NumPy, Pandas/Polars, SciPy, Matplotlib) -At least 3 years of experience developing and managing Cloud Infrastructure on AWS services (VPC, EC2, S3, RDS, etc.) -Experience administering Kubernetes and related tools (e.g. Helm, Karpenter, Traefik) to support production workloads -At least 3 years of professional experience in a DevOps, ML Ops, or Software Engineering role -Educational background or work experience in ultrasound, signal processing, or similar -Outstanding technical communication, analysis and problem-solving abilities -Experience managing hybrid or on-prem infrastructure -Background in ML, especially in Computer Vision for engineering applications -Knowledge of databases, SQL vs NoSQL, data pipelines, and data governance -Bachelor’s degree in Science, Engineering, or Mathematics -Proficiency in Python programming -Familiarity with Linux and Shell Scripting

People Also Searched For

Benefits

This regular full-time position is on-site in our HQ in North Vancouver, BC, where employees enjoy full access to our facility amenities including a well-equipped gym, squash court, climbing wall, steam room, and more! 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. We allow employees to work on cutting-edge technologies that blend science with real-world applications. We invite you to join our team for the exciting journey ahead as we become the global leader in industrial imaging.

About Darkvision

-Founded in 2013 in North Vancouver, the company pioneered industrial acoustic imaging to peer inside critical assets. -Secured Series A funding and earned a Deloitte Fast 50 award. -Acquired majority stake by Koch Industries to expand and fuel R&D. -Their HADES™ downhole tool captures sub-mm 3D ultrasound data from wells thousands of feet deep at high pressure. -Projects span oil & gas well integrity, pipeline inspection, public infrastructure, and aerospace component analysis. -The in-house stack blends sensor arrays, cloud-based AI, signal processing, and photorealistic defect visualizations. -Known for delivering insights in extreme environments.

Sector Specialisms

Security Clearance

-successful candidates must complete a criminal background check.