Instacart3 days ago

Senior Machine Learning Engineer, Digital Twin Platform

RUB 17,167–18,125per month · before tax

MARKET

15,900 ₽median for this role

Data Scientist · 115 jobs with disclosed pay

5,000half of the offers: 12,796–20,50043,793

At market · above 61% of offers

Canada - Remote (ON

Responsibilities

01Design, develop, and deploy machine learning models that power real-time understanding of in-store inventory levels and shelf stocking dynamics across thousands of retail locations at scale
02Own the full ML lifecycle — from problem framing and data exploration through model training, evaluation, and production deployment — with a focus on quality, reliability, and measurable business impact
03Collaborate cross-functionally with software engineers, computer vision engineers, data scientists, and product leads to bring cutting-edge technologies to the team and drive new product innovation
04Contribute to building and evolving the core infrastructure of the Digital Twin Platform, including systems that ingest data from retail partners and actively collect novel inventory observations to feed the modeling pipeline
05Help define the technical direction of an expanding modeling practice on a high-performance team that operates with startup speed

015+ years of experience developing and deploying machine learning models in production environments
02Strong proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn
03Demonstrated experience with large-scale data pipelines and working with structured and unstructured data at scale
04Experience with cloud infrastructure (AWS, GCP, or Azure) and familiarity with ML platform tooling for model training, versioning, and serving
05Bachelor's degree or higher in Computer Science, Machine Learning, Statistics, or a related technical field, or equivalent practical experience
06Experience working on computer vision, inventory forecasting, demand sensing, or related spatial/temporal modeling problems
07Familiarity with real-time inference systems and the architectural considerations of serving ML models in low-latency, high-throughput environments
08Prior experience in a fast-paced, cross-functional environment where you have independently driven projects from conception through production with limited oversight
09Exposure to retail, supply chain, or e-commerce domains and an understanding of how inventory data flows and is consumed in those contexts
10Experience collaborating closely with computer vision teams or incorporating vision-based signals into broader ML systems