below marketmost similar roles pay hereabove market
This role pays less than
85%
of similar roles. Most pay
$203,900–$254,750
— the blue band above.
At the midpoint, this role pays about
$185k
versus about
$229k
for comparable roles.
Based on 240 similar postings.
Employer
About Instacart
Instacart (Maplebear Inc.) is a North American grocery technology company that operates an online marketplace and delivery service connecting customers with personal shoppers who pick and deliver orders from local retailers. It also sells retail e-commerce software and advertising services to grocers and brands.
Instacart currently has
24 open roles
on FindRole.
Listed pay typically runs
$197,500–$208,500
across 24 roles with salary data.
The Senior Machine Learning Engineer, Fulfillment joins the Enterprise AI organization to design, build, and deploy production machine learning systems that create measurable business impact. Working across the full machine learning lifecycle, you will handle problem formulation, data preparation, feature development, model training, offline evaluation, and production monitoring. You will build reliable, scalable software to solve complex problems within the grocery industry, specifically focusing on high-impact products and business initiatives. Key technologies and skills include Python, SQL, and cloud infrastructure like Google Cloud Platform or Amazon Web Services. You must also utilize Docker, Kubernetes, Airflow, and Spark for containerized services and orchestration. The role requires applying statistical analysis and causal evaluation techniques to improve customer outcomes in a fast-paced e-commerce and logistics environment.
What does a Machine Learning Engineer earn in Remote?
Median $246638 from 39 postings across 13 companies.
Design, implement, and deploy machine learning models and services for high-impact Enterprise AI products.
Own the full machine learning lifecycle from problem formulation and data preparation to production rollout.
Build reliable, scalable, and maintainable software using Python and production-quality engineering practices.
Develop and operate machine learning services on cloud infrastructure with robust monitoring and alerting.
Use experimentation and statistical analysis to assess model quality and guide launch decisions.
Shape technical direction through architecture discussions and evaluation of modeling approaches and infrastructure tradeoffs.
Partner with stakeholders to define success metrics and translate ambiguous opportunities into scalable solutions.
What we're looking for
Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related technical field, or equivalent practical experience.
Master’s or PhD in Computer Science, Engineering, Mathematics, Statistics, or a related quantitative field (preferred).
Five or more years of professional experience developing and deploying machine learning models or systems in production.
Proficiency in Python and SQL, including experience with production-quality code, automated testing, profiling, debugging, and code review.
Experience with the complete machine learning lifecycle, including data preparation, feature engineering, model evaluation, experimentation, deployment, and production monitoring.
Experience building and operating machine learning services or data products in a cloud environment such as Google Cloud Platform or Amazon Web Services.
Experience with containerized services and production software development practices, including Docker, continuous integration, monitoring, alerting, and incident response.
Demonstrated ability to work with product, engineering, data science, and business stakeholders to define requirements and deliver measurable outcomes.
Python
SQL
Machine Learning
Google Cloud Platform
Amazon Web Services
Docker
Kubernetes
Airflow
Spark
C++
Java
Go
Feature Stores
Distributed Data Processing
Streaming Systems
Model Serving
Monitoring
Alerting
Observability
Machine Learning
Python
Golang
TensorFlow
PyTorch
Deep Learning
Recommendation Systems
Feature Pipelines
Model Training
Regression Analysis
Hypothesis Testing
Production Systems