Lead Machine Learning Engineer - Merchandising AI (ML Ops)

Target is hiring a Lead Machine Learning Engineer - Merchandising AI (ML Ops) for a remote role open to applicants in United States. It pays $132k-$238k a year and Level rates it ; you can apply on Target.

Level's read on this job

On Level's scale, this machine learning engineer role is Builds AI ●●●●: the job is building AI systems.
This is listed as a remote role.
The posting mentions Vertex AI and MLOps among its tools and skills.
Target has 1,600+ other open roles on Level.
Posted within the past two weeks.

AI in this role

vertex-ai

ml-ops

The pay range is $132,000.00 - $238,000.00

Pay is based on several factors which vary based on position. These include labor markets and in some instances may include education, work experience and certifications. In addition to your pay, Target cares about and invests in you as a team member, so that you can take care of yourself and your family. Target offers eligible team members and their dependents comprehensive health benefits and programs, which may include medical, vision, dental, life insurance and more, to help you and your family take care of your whole selves. Other benefits for eligible team members include 401(k), employee discount, short term disability, long term disability, paid sick leave, paid national holidays, and paid vacation. Find competitive benefits from financial and education to well-being and beyond at https://corporate.target.com/careers/benefits.

JOIN US AS A LEAD MACHINE LEARNING ENGINEER – MERCHANDISING AI

About Us:

Working at Target means helping all families discover the joy of everyday life. We bring that vision to life through our values and culture. Learn more about Target here.

A role with Target Data Sciences means the chance to help develop and manage state of the art predictive algorithms that use data at scale to automate and optimize decisions at scale. Whether you join our Applied Data Sciences or Machine Learning teams, you’ll be challenged to harness Target’s impressive data breadth to build the algorithms that power solutions our partners in Digital Marketing, Supply Chain Optimization, Advanced AI, Search and Personalization rely on. Every Scientist on Target’s Data Sciences team can expect modeling and data science, software/product development of highly performant code for model performance at scale.

As the Lead Machine Learning Engineer – Merchandising, you will join a Data Sciences team responsible for creating and optimizing the data and models used to build a world-class merchandising product. You will play a crucial role in designing, implementing, and optimizing production machine learning solutions. We will also expect you to understand best practice software design, participate in code reviews, and create a maintainable well-tested codebase with relevant documentation. At an organizational level, you may conduct training sessions, present work to technical and non-technical peers/leaders, build knowledge on business priorities/strategic goals and leverage this knowledge while building requirements and solutions for each business need.

Core responsibilities of this job are articulated within this job description. Job duties may change at any time due to business needs.

About you:

4-year degree in Quantitative disciplines (Science, Technology, Engineering, Mathematics) or equivalent experience
MS in Computer Science, Applied Mathematics, Statistics, Physics or equivalent work or industry experience in applied machine learning
5 plus years of experience in end-to-end machine learning application development, including data pipelining, model optimization, deployment and API design
Extensive experience with applied machine learning frameworks and the ability to developing highly distributed machine learning systems at scale
Highly proficient in Python programming
Strong understanding of CI/CD practices for machine learning systems and ML Ops principles
Extensive experience with Google Cloud’s Vertex AI and/or the broader cloud-based ML ecosystem
Demonstrated knowledge of various testing frameworks and containerization like Docker and Kubernetes
Lead the development of agentic AI solutions that connect data, tools, APIs and business rules into end-to-end decisioning workflows
Demonstrated experience in REST API design and development
Extensive understanding of Big Data technologies including Hadoop, Spark and Kafka
Proven ability to collaborate with data scientists, software engineers and product managers to deliver scalable machine learning solutions
Excellent communication skills with an ability to tell data-driven stories through visualizations and narratives
Ability to mentor engineers and help establish engineering standards, architecture patterns, and best practices for AI/ML and cloud-native development.
Self-driven, results-oriented and able to meet tight deadlines
Motivated team player who thrives in a collaborative global environment

This position may be considered for a Remote or Hybrid (known internally at Target as "Flex for Your Day") work arrangement based on Target's needs.  A Remote work arrangement means the team member works full-time from home or an alternate location that's not a Target location, does not have a desk at a Target location and may travel to HQ up to 4 times a year.  A Hybrid/Flex for Your Day work arrangement means the team member's core role may be performed either remote or onsite at a Target location depending upon what your role, team and tasks require for that day. Work duties cannot be performed outside of the country of the primary work location, unless otherwise prescribed by Target.

Benefits Eligibility

Please paste this url into your preferred browser to learn about benefits eligibility for this role: https://tgt.biz/BenefitsForYou_E

Americans with Disabilities Act (ADA)

In compliance with state and federal laws, Target will make reasonable accommodations for applicants with disabilities. If a reasonable accommodation is needed to participate in the job application or interview process, please reach out to candidate.accommodations@HRHelp.Target.com. Non-accommodation-related requests, such as application follow-ups or technical issues, will not be addressed through this channel.  

Application deadline is : 10/29/2026

How we rate this

Lead Machine Learning Engineer - Merchandising AI (ML Ops) at Target rates 91 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.

Classification

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.

Key facts

Lead Machine Learning Engineer - Merchandising AI (ML Ops) is an open job at Target.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $132k-$238k a year.
The role is based in 7000 Target Pkwy N,NCD-0375 Brooklyn Park,MN 55445 and is marked remote.
The job was posted on October 9, 2026.
Seniority: lead.
Applications are handled by Target. Apply on Target

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

ML OpsVertex AI

Also common in machine learning engineer jobs at this level

PyTorch LLM TensorFlow Deep learning AWS Generative AI

Pay

This job starts at: $132k
Typical machine learning engineer job: $199k

This posting starts 34% below the typical machine learning engineer job. See machine learning engineer jobs

Questions you could be asked

How do you monitor a model once it's live, and how do you know it needs retraining?
Walk me through how you've used Vertex AI in your day-to-day work.
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: ML Ops and Vertex AI. 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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