We are seeking a Senior Machine Learning Engineer to design, build, and deploy production machine learning systems that create measurable business impact. You will work across the full lifecycle of machine learning products, from problem formulation and data development through experimentation, deployment, monitoring, and continuous improvement.
This role is ideal for an engineer who combines strong machine learning expertise with excellent software engineering fundamentals. You will collaborate closely with product managers, data scientists, platform engineers, and business partners to translate ambiguous opportunities into reliable, scalable solutions. The work will be fast-paced and highly collaborative, with opportunities to solve challenging problems at significant scale and see your systems used in the real world.
About the Job
Design, implement, and deploy machine learning models and services that support high-impact Enterprise AI products and business initiatives.
Own the full machine learning lifecycle, including problem formulation, data preparation, feature development, model training, offline evaluation, experimentation, production rollout, and ongoing performance monitoring.
Build reliable, scalable, and maintainable software in Python and related technologies, applying strong practices for testing, profiling, code review, documentation, and operational readiness.
Partner with product, engineering, data science, and business stakeholders to define success metrics, prioritize work, and deliver solutions that improve customer and business outcomes.
Develop and operate machine learning services on cloud infrastructure, with appropriate monitoring, alerting, observability, and safeguards for production performance and reliability.
Use experimentation, statistical analysis, and causal or counterfactual evaluation techniques to assess model quality and guide launch decisions.
Help shape technical direction through design reviews, architecture discussions, and thoughtful evaluation of modeling approaches, infrastructure choices, and tradeoffs.
Contribute to a strong engineering culture by sharing knowledge, mentoring teammates, improving development practices, and participating in operational support for critical services.
This is a fast-moving environment where priorities, data, and business needs can evolve quickly. Success requires comfort with ambiguity, pragmatic prioritization, sound technical judgment, and a willingness to move between strategic problem-solving and hands-on implementation.
About You
You bring a strong foundation in machine learning and software engineering, along with the ability to communicate clearly and work effectively across disciplines. You take ownership from idea through production, are motivated by measurable impact, and enjoy solving challenging problems with practical, scalable solutions.
Minimum Qualifications
Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related technical field, or equivalent practical experience.
Five or more years of professional experience developing and deploying machine learning models or systems in production.
Proficiency in Python and SQL, including experience writing production-quality code with automated testing, profiling, debugging, and code review practices.
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.
Preferred Qualifications
Master’s or PhD in Computer Science, Engineering, Mathematics, Statistics, or a related quantitative field.
Experience designing machine learning systems for large-scale, high-throughput, or low-latency applications.
Familiarity with distributed data processing, streaming systems, feature stores, or real-time feature computation.
Experience with model serving, orchestration, and machine learning platforms such as Kubernetes, Airflow, Spark, or comparable technologies.
Experience designing simulation, experimentation, or counterfactual evaluation frameworks for machine learning and decision systems.
Proficiency in a performance-oriented language such as C++, Java, or Go.
Experience mentoring engineers, leading cross-functional projects, or setting technical direction for machine learning initiatives.
Experience working in e-commerce, marketplaces, logistics, retail, advertising, or another domain involving complex and evolving business constraints.
#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.
CA, NY, CT, NJ
$207,000—$218,500 USD
WA
$198,000—$209,000 USD
OR, DE, ME, MA, MD, NH, RI, VT, DC, PA, VA, CO, TX, IL, HI
$190,000—$200,500 USD
All other states
$173,000—$182,500 USD
