Entry Level Machine Learning Manager Jobs
New grad machine learning manager jobs are open to recent graduates and entry level candidates with zero to two years of experience, where a strong portfolio or internship work can matter more than a long resume. Most openings are on-site roles across Technology & Software, Science & Research, and Electronics & Hardware, with employers like TikTok, Pinterest, and Apple hiring at this level now.
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Showing 5 of 132+ Entry Level Machine Learning Manager jobs
Salary Range: $184,800 - $196,350 per year. Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in the posting reflect the annual base salary only and do not include equity.
Who We Are
Wayfair’s Advertising business is rapidly expanding, adding hundreds of millions of dollars in profits to Wayfair. We build Sponsored Products, Display & Video Ad offerings that cater to a variety of Advertiser goals while showing highly relevant and engaging Ads to millions of customers. We are evolving our Ads Platform to empower advertisers across all sophistication levels to grow their business on Wayfair at a strong, positive ROI and are leveraging state of the art Machine Learning techniques.
The Advertising Demand Science team is central to this effort. We leverage machine learning and generative AI to streamline campaign workflows, delivering impactful recommendations on budget allocation, target Return on Ad Spend (tROAS), and SKU selection. We explore the economics of our advertising system to understand impacts of advertisers’ changes to their outcomes. Additionally, we are developing intelligent systems for creative optimization and exploring agentic frameworks to further simplify and enhance advertiser interactions.
What you’ll do
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Provide technical leadership in the development of agentic systems by advancing the state-of-the-art in machine learning techniques to support recommendations for Ads campaigns and other optimizations.
-
Design, build, deploy and refine extensible, reusable, large-scale, and real-world platforms that optimize our ads experience.
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Work cross-functionally with commercial stakeholders to understand business problems or opportunities and develop appropriately scoped machine learning solutions.
-
Collaborate closely with various engineering, infrastructure, and machine learning platform teams to ensure adoption of best-practices in how we build and deploy scalable machine learning services.
-
Identify new opportunities and insights from the data (where can the models be improved? What is the projected ROI of a proposed modification?).
-
Research new developments in advertising, sort and recommendations research and open-source packages, and incorporate them into our internal packages and systems.
-
Be obsessed with the customer and maintain a customer-centric lens in how we frame, approach, and ultimately solve every problem we work on.
Who you are
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Minimum 2+ years of experience with PhD, or 4+ years of industry experience with MS, or 6+ years of experience with a BS in a quantitative STEM field.
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2+ years of experience developing, evaluating, optimizing and deploying machine learning models, with a focus on advertising, bidding, recommendations, ranking, or personalization.
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Strong theoretical understanding of agentic systems and machine learning applied to large-scale ML problems.
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Proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-Learn.
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Familiarity with data processing and ML pipeline orchestration (Airflow, Kubeflow, MLflow).
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Strong coding skills and familiarity with building scalable ML systems in cloud environments (AWS, GCP, Azure).
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Ability to work with others to help design experiments and analyze results using A/B testing and statistical techniques.
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Excellent communication skills, with the ability to explain complex ML concepts to non-technical stakeholders and drive data-driven decisions.
Nice to have
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Familiarity with Machine Learning platforms offered by Google Cloud and how to implement them on a large scale (e.g. BigQuery, GCS, Dataproc, AI Notebooks).
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Experience in advertising.
-
Experience in Economics problems common in e-commerce.
-
Hands-on Agentic experience with chat-based products deployed to consumers.
Salary Range: $184,800 - $196,350 per year. Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in the posting reflect the annual base salary only and do not include equity.
Who We Are
Wayfair’s Advertising business is rapidly expanding, adding hundreds of millions of dollars in profits to Wayfair. We build Sponsored Products, Display & Video Ad offerings that cater to a variety of Advertiser goals while showing highly relevant and engaging Ads to millions of customers. We are evolving our Ads Platform to empower advertisers across all sophistication levels to grow their business on Wayfair at a strong, positive ROI and are leveraging state of the art Machine Learning techniques.
The Advertising Demand Science team is central to this effort. We leverage machine learning and generative AI to streamline campaign workflows, delivering impactful recommendations on budget allocation, target Return on Ad Spend (tROAS), and SKU selection. We explore the economics of our advertising system to understand impacts of advertisers’ changes to their outcomes. Additionally, we are developing intelligent systems for creative optimization and exploring agentic frameworks to further simplify and enhance advertiser interactions.
What you’ll do
-
Provide technical leadership in the development of agentic systems by advancing the state-of-the-art in machine learning techniques to support recommendations for Ads campaigns and other optimizations.
-
Design, build, deploy and refine extensible, reusable, large-scale, and real-world platforms that optimize our ads experience.
-
Work cross-functionally with commercial stakeholders to understand business problems or opportunities and develop appropriately scoped machine learning solutions.
-
Collaborate closely with various engineering, infrastructure, and machine learning platform teams to ensure adoption of best-practices in how we build and deploy scalable machine learning services.
-
Identify new opportunities and insights from the data (where can the models be improved? What is the projected ROI of a proposed modification?).
-
Research new developments in advertising, sort and recommendations research and open-source packages, and incorporate them into our internal packages and systems.
-
Be obsessed with the customer and maintain a customer-centric lens in how we frame, approach, and ultimately solve every problem we work on.
Who you are
-
Minimum 2+ years of experience with PhD, or 4+ years of industry experience with MS, or 6+ years of experience with a BS in a quantitative STEM field.
-
2+ years of experience developing, evaluating, optimizing and deploying machine learning models, with a focus on advertising, bidding, recommendations, ranking, or personalization.
-
Strong theoretical understanding of agentic systems and machine learning applied to large-scale ML problems.
-
Proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-Learn.
-
Familiarity with data processing and ML pipeline orchestration (Airflow, Kubeflow, MLflow).
-
Strong coding skills and familiarity with building scalable ML systems in cloud environments (AWS, GCP, Azure).
-
Ability to work with others to help design experiments and analyze results using A/B testing and statistical techniques.
-
Excellent communication skills, with the ability to explain complex ML concepts to non-technical stakeholders and drive data-driven decisions.
Nice to have
-
Familiarity with Machine Learning platforms offered by Google Cloud and how to implement them on a large scale (e.g. BigQuery, GCS, Dataproc, AI Notebooks).
-
Experience in advertising.
-
Experience in Economics problems common in e-commerce.
-
Hands-on Agentic experience with chat-based products deployed to consumers.
See All 132+ Entry Level Machine Learning Manager Jobs
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Find JobsEntry Level Machine Learning Manager Job Market
Who's Hiring
- TikTok13
- Pinterest8
- Apple5
- Anthropic5
- Abridge3
Top Industries Hiring
- Technology & Software59
- Science & Research15
- Electronics & Hardware9
- Banking & Financial Services8
- Biotechnology & Pharmaceuticals8
Entry Level Machine Learning Manager Jobs: Frequently Asked Questions
How do I get an entry level machine learning manager job?
Employers hiring at the entry level typically look for candidates who can demonstrate hands-on experience through internships, academic projects, or independent portfolio work involving model development, data pipelines, or team coordination. Familiarity with Python, ML frameworks, and basic project management signals readiness. Targeting roles that explicitly list zero to two years of experience and emphasize growth potential gives new candidates the strongest shot.
Which companies hire entry level machine learning managers?
Companies hiring entry level machine learning managers right now include TikTok, Pinterest, and Apple, based on current listings on Migrate Mate as of September 2026. A mix of technology firms, startups, and enterprise organizations post at this level, particularly those building out ML teams and willing to develop candidates with strong foundational skills.
Are there remote entry level machine learning manager jobs?
Yes, though on-site and hybrid postings make up a significant share of openings at this stage. About 27% of entry level machine learning manager openings are remote or hybrid as of September 2026, so candidates open to flexible work settings will find a solid range of options alongside fully in-person roles.
Are these new grad machine learning manager jobs?
Yes, this page includes new grad, recent graduate, and junior machine learning manager roles alongside other entry level postings. A new grad-friendly posting typically welcomes zero to two years of experience, counts internships or academic project work toward requirements, and may accept a strong portfolio in place of full-time history. Look for language like "new grad welcome" or "junior" in the listing.
Which industries hire the most entry level machine learning managers?
Entry Level machine learning manager roles concentrate in Technology & Software, Science & Research, and Electronics & Hardware, based on current listings on Migrate Mate as of September 2026. These sectors tend to drive hiring at the entry level because they are actively scaling ML capabilities and often build structured programs that develop junior talent into full managers over time.