Entry Level Machine Learning Scientist Jobs
New grad machine learning scientist jobs welcome recent graduates and entry level candidates with zero to two years of experience, where a strong portfolio or internship project can carry more weight than years on a resume. Most openings are on-site and hybrid roles across Technology & Software, Biotechnology & Pharmaceuticals, and Social Media, with employers like TikTok, Lila Sciences, and Nuro hiring at this level now.
Find JobsOverview
Showing 5 of 25+ Entry Level Machine Learning Scientist 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
-
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.
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 25 Entry Level Machine Learning Scientist Jobs
Find roles that match your experience and apply in just a few clicks.
Find JobsEntry Level Machine Learning Scientist Job Market
Who's Hiring
Top Industries Hiring
- Technology & Software11
- Biotechnology & Pharmaceuticals5
- Social Media1
- Medical Devices1
- Science & Research1
Entry Level Machine Learning Scientist Jobs: Frequently Asked Questions
How do I get an entry level machine learning scientist job?
Entry level machine learning scientist roles go to candidates who can demonstrate applied skills through projects, coursework, or internships. Employers at this stage look for proficiency in Python, experience with frameworks like PyTorch or TensorFlow, and a GitHub portfolio showing real model work. A master's degree or strong undergraduate research helps, but hands-on project work often matters just as much to hiring teams.
Which companies hire entry level machine learning scientists?
Companies hiring entry level machine learning scientists right now include TikTok, Lila Sciences, and Nuro, based on current listings on Migrate Mate as of September 2026. Hiring at this level comes from a mix of large tech firms, AI-focused startups, and enterprise companies building or expanding their data science teams.
Are there remote entry level machine learning scientist jobs?
Yes, though on-site and hybrid roles still make up the majority of openings. About 18% of entry level machine learning scientist openings are remote or hybrid as of September 2026, so candidates open to flexible locations will find more options than those searching for fully remote work only.
Are these new grad machine learning scientist jobs?
Yes, these listings include new grad, recent graduate, and junior machine learning scientist roles. A new grad friendly posting typically welcomes zero to two years of experience, accepts internships or research assistantships in place of full-time history, and explicitly values a strong portfolio or capstone project. If a listing does not require prior professional experience, it fits the new grad category.
Which industries hire the most entry level machine learning scientists?
Entry Level machine learning scientist roles concentrate in Technology & Software, Biotechnology & Pharmaceuticals, and Social Media, 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 and need early-career talent to support model development, data pipelines, and research initiatives.