Entry Level Machine Learning Engineer Jobs
New grad machine learning engineer jobs attract 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, remote, and hybrid roles across Technology & Software, Science & Research, and Electronics & Hardware, with employers like TikTok, Pinterest, and Scale AI hiring at this level now.
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Showing 5 of 162+ Entry Level Machine Learning Engineer 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.
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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.
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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 162+ Entry Level Machine Learning Engineer Jobs
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Find JobsEntry Level Machine Learning Engineer Job Market
Who's Hiring
- TikTok15
- Pinterest8
- Scale AI6
- Apple5
- Anthropic4
Top Industries Hiring
- Technology & Software69
- Science & Research16
- Electronics & Hardware13
- Banking & Financial Services12
- Artificial Intelligence9
Entry Level Machine Learning Engineer Jobs: Frequently Asked Questions
How do I get an entry level machine learning engineer job?
Build a portfolio of projects that demonstrate core skills: training and evaluating models, working with real datasets, and writing clean Python code. Employers at this level look for familiarity with frameworks like PyTorch or TensorFlow, foundational knowledge of statistics and linear algebra, and evidence you can ship something, whether that comes from coursework, a capstone project, a Kaggle competition, or an internship.
Which companies hire entry level machine learning engineers?
Companies hiring entry level machine learning engineers right now include TikTok, Pinterest, and Scale AI, based on current listings on Migrate Mate as of September 2026. At this level, hiring covers large technology firms with structured new-grad programs, fast-growing AI startups, and enterprise companies building out their first ML teams.
Are there remote entry level machine learning engineer jobs?
Yes, though fully remote roles at the entry level are less common than for senior positions. About 27% of entry level machine learning engineer openings are remote or hybrid as of September 2026, so candidates who need location flexibility still have meaningful options while many employers prefer on-site for new engineers early in their career.
Are these new grad machine learning engineer jobs?
Yes, the listings here include new grad, recent graduate, and junior machine learning engineer roles. A posting is typically new-grad friendly when it welcomes zero to two years of experience and accepts internships, academic projects, or a strong portfolio in place of full-time work history. Look for language like "entry level", "new grad", or "junior" in the job description as a reliable signal.
Which industries hire the most entry level machine learning engineers?
Entry Level machine learning engineer roles concentrate in Technology & Software, Science & Research, and Electronics & Hardware, based on current listings on Migrate Mate as of September 2026. These sectors drive hiring at this level because they are actively building ML infrastructure, have the data volume to support model development, and run new-grad or rotational programs designed to develop junior engineers quickly.