Senior Machine Learning Engineer, Operations Research
Instacart is hiring a Senior Machine Learning Engineer, Operations Research for a remote role open to applicants in United States. It pays $207k-$219k a year and Level rates it ; you can apply on Instacart.
Level's read on this job
- On Level's scale, this machine learning engineer role is Builds AI ●●●●: the job is building AI systems.
- The listed pay starts close to the typical machine learning engineer posting on Level.
- The posting lists this role as remote.
- Tools and skills the posting cites include TensorFlow, scikit-learn and Keras.
- Level lists 88 other open roles at Instacart.
AI in this role
We're transforming the grocery industry
At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers.
Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.
Instacart is a Flex First team
There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work.
Overview:
We are looking for a Senior Machine Learning Engineer with a strong Operations Research background to join the Service Availability & Routing team within Instacart's Logistics organization. In this role, you will work at the intersection of combinatorial optimization, mathematical programming, and AI to solve high-impact problems in the fulfillment space — including order batching, shopper routing, service availability prediction, and real-time assignment. You'll partner closely with engineering, product, and data science to ship models and algorithms that directly influence Instacart's profitability and shopper experience at scale.
The Logistics & ML group is responsible for the intelligence and execution behind Instacart’s fulfillment system. The team optimizes a multi-sided marketplace to ensure customers get their orders on-time and in high quality, shoppers get efficient and fulfilling work, and retailers and consumer brands get reasonable business. The team tackles hard problems in a variety of spaces, such as matching, pricing, and geospatial, as well as foundational problems executing on a high throughput system with dynamic data.
About the Job:
- Design, develop, and deploy machine learning solutions to tackle practical challenges in the marketplace.
- Collaborate closely with product managers, data scientists, and backend engineers to deeply understand business needs and create impactful ML applications.
- Actively engage with diverse stakeholders to ensure that solutions are well-integrated and aligned with business goals.
- Push the envelope on our operational efficiency by continually refining and advancing our algorithms and models.
About You:
Minimum Qualifications:
- 3+ years of industry experience using machine learning to solve real-world problems with large datasets
- Have strong programming skills in Python and fluency in data manipulation (SQL, Pandas) and Machine Learning (scikit-learn, XGBoost, Keras/Tensorflow) tools
- Have strong analytical skills and problem-solving ability
- Are a strong communicator who can collaborate with diverse stakeholders across all levels
- Graduate degree (masters or PhD) in Operations Research or Industrial Engineering or closely related field
Preferred Qualifications:
- Knowledge of deep learning frameworks and methodologies
- Experience in applying machine learning and optimization techniques to solve marketplace problems
#LI-Remote
Instacart provides highly market-competitive compensation and benefits in each location where our employees work. This role is remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex First remote work policy here.
Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is eligible for a new hire equity grant as well as annual refresh grants. Please read more about our benefits offerings here.
For US based candidates, the base pay ranges for a successful candidate are listed below.
How we rate this
Senior Machine Learning Engineer, Operations Research at Instacart rates 86 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Pay
- This job starts at
- $207k
- Typical machine learning engineer job
- $200k
This posting starts close to the typical machine learning engineer job. See machine learning engineer jobs
Questions you could be asked
- What's a project where you used TensorFlow hands-on?
- Walk me through how you've used scikit-learn in your day-to-day work.
- What are the limits of Keras that you've run into, and how did you work around them?
- What's a project where you used XGBoost hands-on?
- How would you decide a model or AI system is ready to ship?
Adapt your resume
- List these exact terms on your resume: TensorFlow, scikit-learn, Keras, and XGBoost. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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