Lead Machine Learning Engineer - Personalization/Recommender Systems (Python, ML Ops, Vertex AI)
JOIN TARGET AS A LEAD MACHINE LEARNING ENGINEER
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.
Join our global in-house Tech and Data Sciences team of more than 5,000 software engineers, applied data scientists, ML engineers and product managers striving to make Target the most convenient, safe and joyful place to shop. We use agile practices and leverage open-source software to adapt and build best-in-class technology for our team members and guests. We do so with a focus on diversity and inclusion, experimentation and continuous learning.
As Lead Machine Learning Engineer, you will join a Data Sciences team responsible for creating personalized recommendations on Target.com and the Target App. 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 will 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
- 5 plus years' of experience in end-to-end Machine Learning application development, including data pipelining, model optimization, deployment, and API design
- Highly proficient programming in Python
- Experience with ML frameworks such as Pytorch, TensorFlow, xgboost, sklearn, and ONNX
- Experience with one or more cloud ML services such as Vertex AI, Azure ML or Sagemaker
- Experience using distributed training frameworks like Spark, Ray, TensorFlow Distributed
- Experience with serving frameworks such as TorchServe/TensorFlow or Serving/FastAPI
- Good understanding of Big Data tech, specifically Hadoop ecosystem – Spark, Kafka, Hive, etc.
- Experience creating and maintaining CI/CD pipelines for automated model deployment and testing
- Work in partnership with applied data scientists, software engineers and product managers to understand the business requirements - translate to machine learning solutions at scale
- Excellent communication skills with the ability to clearly tell data driven stories through appropriate visualizations, graphs, and narratives
- Self-driven and results oriented; able to meet tight timelines
- Motivated, team player with ability to collaborate effectively across global team
- Experience in mentoring the junior team members ML Ops skillsets and career development
This position will operate as a Hybrid/Flex for Your Day work arrangement based on Target's needs. A Hybrid/Flex for Your Day work arrangement means the team member's core role will need to be performed both onsite at the Target HQ location in Sunnyvale or the role is assigned to and virtually, 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.
Target will consider for employment qualified applicants with criminal histories in a manner consistent with the San Francisco and City of Los Angeles Fair Chance Ordinances.
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.
Target is a general merchandise retailer with stores in all 50 states and the District of Columbia. The company operates nearly 2,000 locations in the United States and offers a mix of owned and national brands that guests can shop in stores, online, or via home delivery.
Target focuses on helping families discover the joy of everyday life while reinvesting a portion of its profits into the communities it serves through products, cash contributions, and philanthropic programs.
March 3, 2026
5-7 business days
After submission
- •Role explicitly requires end-to-end production ML work: data pipelines, model optimization, deployment, serving frameworks, and CI/CD for automated model deployment (strong workflow integration).
- •Extensive explicit tool list (PyTorch, TensorFlow, Vertex AI, Spark, Ray, TorchServe, etc.) indicates high tool proficiency and hands-on applied AI usage.
- •Position requires translating business requirements into ML solutions and presenting to non-technical stakeholders, showing strategic application of AI to business goals.
This is a strong, senior in-house role at a very stable, large employer with explicit compensation, a benefits link, and clear technical expectations. The posting strongly supports safety (company stability) and esteem (lead responsibilities, visibility), and provides adequate signals for physiological needs (salary, benefits link). Team/culture and growth signals are present but less detailed — DE&I and continuous learning are mentioned, but concrete career-path or development-budget details are missing. The role's breadth and the note about changing duties suggest candidates should probe resourcing, scope stability, and work-life expectations during interviews.
- •Broad and deep technical + organizational expectations (end-to-end ML, distributed training, CI/CD, serving, mentoring, presentations to leaders) may indicate a high workload and heavy breadth of responsibility — clarify resourcing and support.
- •"Core responsibilities of this job are articulated within this job description. Job duties may change at any time due to business needs." — language that permits frequent scope changes, which can signal role volatility.
- •Work-life expectations are vague: phrases like "self-driven... able to meet tight timelines" without clear boundaries or explicit PTO/working-hour practices could imply high time pressure or periodic long hours.
- •Clear, competitive compensation range provided ($128,000-$277,000).
- •Benefits eligibility and ADA accommodation links are explicitly provided.
- •Position is with a large, established employer (Target) with ~2,000 stores and an internal Tech & Data Sciences organization of ~5,000 — positive stability signal.
- •Can you describe the immediate team structure (size, roles) this Lead ML Engineer will work with, and who this role reports to?
- •What are the top 2–3 business problems or projects this hire is expected to deliver in the first 6 months, and how will success be measured?
- •What formal support exists for professional development (training budget, conference allowance, dedicated learning time, internal mentorship or rotation programs)?
Location
Salary Range
$128k - $277k
Employment Type
Full-time
Experience Level
Senior
Posted
December 3, 2025 (1 week ago)
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