... grad machine learning experience * Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques * Experience designing and analyzing ...
... grad machine learning experience * Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques * Experience designing and analyzing ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
... grad machine learning experience * Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques * Experience designing and analyzing ...
... grad machine learning experience * Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques * Experience designing and analyzing ...
... grad machine learning experience * Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques * Experience designing and analyzing ...
... grad machine learning experience * Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques * Experience designing and analyzing ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
... grad machine learning experience * Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques * Experience designing and analyzing ...
... grad machine learning experience * Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques * Experience designing and analyzing ...
... grad machine learning experience * Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques * Experience designing and analyzing ...
... grad machine learning experience * Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques * Experience designing and analyzing ...
... grad machine learning experience * Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques * Experience designing and analyzing ...
... grad machine learning experience * Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques * Experience designing and analyzing ...
... grad machine learning experience * Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques * Experience designing and analyzing ...
... grad machine learning experience * Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques * Experience designing and analyzing ...
... grad machine learning experience * Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques * Experience designing and analyzing ...
... grad machine learning experience * Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques * Experience designing and analyzing ...
... grad machine learning experience * Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques * Experience designing and analyzing ...
... grad machine learning experience * Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques * Experience designing and analyzing ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
... grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience * Experience developing machine learning models for relevance ranking ...
New Grad Machine Learning information
See salary details
$25.5K - $31.2K
5% of jobs
$33.1K is the 25th percentile. Wages below this are outliers.
$31.2K - $36.9K
59% of jobs
$36.9K - $42.5K
9% of jobs
$43K is the 75th percentile. Wages above this are outliers.
$42.5K - $48.2K
17% of jobs
$48.2K - $53.9K
4% of jobs
$53.9K - $59.6K
2% of jobs
$59.6K - $65.3K
3% of jobs
$65.3K - $71K
0% of jobs
$71K - $76.6K
0% of jobs
$76.6K - $82.3K
0% of jobs
$82.3K - $88K
0% of jobs
$25.5K
$42.6K
$88K
How much do new grad machine learning jobs pay per year?
As of Sep 12, 2026, the average yearly pay for new grad machine learning in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.
What is a new grad machine learning?
New Grad Machine Learning roles are entry-level positions designed for recent graduates who have studied machine learning, artificial intelligence, data science, or related fields. These positions typically involve working with experienced data scientists and engineers to develop, implement, and improve machine learning models and algorithms. New grads in these roles often contribute to projects involving data preprocessing, model training, evaluation, and deployment. The goal is to help new graduates gain hands-on experience and grow their skills in a real-world setting while contributing to the organization's AI initiatives.
What skills and qualifications are needed to thrive as a new grad machine learning?
To thrive as a New Grad Machine Learning Engineer, you need a solid foundation in mathematics, statistics, and programming (especially Python), typically supported by a degree in computer science or a related field. Familiarity with machine learning frameworks such as TensorFlow or PyTorch, version control systems like Git, and coursework or certification in data science are highly beneficial. Strong problem-solving abilities, curiosity, and effective communication skills help you collaborate and convey complex technical concepts to diverse teams. These skills and qualities are essential for developing innovative models, ensuring project success, and integrating seamlessly into fast-paced tech environments.
What challenges do new graduates face when starting out in a machine learning role, and how can they overcome them?
New grad machine learning engineers often encounter challenges such as bridging the gap between academic knowledge and practical, production-level projects. Adapting to real-world data issues, collaborating with cross-functional teams, and understanding scalable deployment can be daunting at first. To overcome these, it's helpful to seek mentorship, proactively ask questions, and dedicate time to learning best practices in code versioning, model evaluation, and team communication. Engaging in code reviews and participating in team discussions can also accelerate the learning curve and foster professional growth.
What is the difference between New Grad Machine Learning vs Data Scientist?
| Aspect | New Grad Machine Learning | Data Scientist |
|---|---|---|
| Required Credentials | Bachelor's in CS, Data Science, or related field; some internships | Bachelor's or Master's in CS, Statistics, or related; some experience |
| Work Environment | Entry-level, team-focused, research and development | Data analysis, modeling, cross-functional collaboration |
| Employer & Industry Usage | Tech companies, startups, research labs | Tech, finance, healthcare, consulting firms |
New Grad Machine Learning roles typically focus on foundational skills, internships, and entry-level tasks, while Data Scientist positions often require more experience in data analysis and statistical modeling. Both roles are common in tech industries, but Data Scientists usually handle broader data analysis responsibilities.
More about New Grad Machine Learning jobs
What cities are hiring for New Grad Machine Learning jobs?
Cities with the most New Grad Machine Learning job openings:
What states have the most New Grad Machine Learning jobs?
States with the most job openings for New Grad Machine Learning jobs include:
What job categories do people searching New Grad Machine Learning jobs look for?
The top searched job categories for New Grad Machine Learning jobs are:
- Machine Learning Algorithms
- International Machine Learning Engineer
- Internship Machine Learning
- Founding Machine Learning Engineer
- Reinforcement Learning Robotics
- Contract Apple Machine Learning Engineer
- Deep Learning Quantization
- Artificial Intelligence And Machine Learning
- Artificial Intelligence Machine Learning Engineer
- Senior Tesla Machine Learning Engineer
What other helpful pages are available for New Grad Machine Learning?
Other pages related to New Grad Machine Learning:
Machine Learning Engineer, Causal Inference, Level 5
Palo Alto, CA
Snapchat
 • Marketing • 1 - 5K employees1,001 - 5,000 employees
Full-time
Medical
Re-posted 13 days ago
Job description
Snap Inc is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together.
The Company operates Snapchat, a visual messaging app that enhances your relationships with friends, family, and the world, and Specs Inc., a wholly-owned subsidiary dedicated to making computing more human, in addition to Bitmoji, Saturn, and other digital services.
Snap Engineering teams build fun and technically sophisticated products that reach hundreds of millions of Snapchatters around the world, every day. We're deeply committed to the well-being of everyone in our global community, which is why our values are at the root of everything we do. We move fast, with precision, and always execute with privacy at the forefront.
We're looking for a Machine Learning Engineer to join Snap Inc!
What you'll do:
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business
Develop and productionize causal machine learning solutions (e.g., uplift modeling, heterogeneous treatment effect estimation) using observational and experimental data
Design, analyze, and interpret A/B tests and quasi-experiments; collaborate closely with product and engineering partners to shape experimentation strategies
Evaluate technical tradeoffs between model complexity, bias/variance, scalability, and interpretability
Conduct code reviews, maintain high engineering standards, and build scalable, maintainable infrastructure
Contribute to rapid iteration cycles while ensuring methodological rigor
Knowledge, Skills & Abilities:
Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables)
Experience with applied data science, including A/B testing, uplift modeling, and experimentation infrastructure
Proficient in Python and common data/machine learning libraries (e.g., pandas, NumPy, scikit-learn, CausalM etc.)
Skilled at solving open-ended problems with a mix of statistical thinking and engineering pragmatism
Comfortable working independently and collaborating across cross-functional teams
Strong communication and mentorship skills; able to translate technical insights for non-technical partners
Minimum Qualifications:
Bachelor's degree in computer science, statistics, economics, or a related technical field, or equivalent practical experience
5+ years of post-Bachelor's experience in machine learning, with hands-on experience in causal inference or experimentation; or Master's degree in a technical field + 4+ year of post-grad machine learning experience; or PhD in a relevant technical field + 2 years of post-grad machine learning experience
Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques
Experience designing and analyzing online experiments (A/B tests) and leveraging causal ML in production systems
Preferred Qualifications:
Advanced degree (MS/PhD) in a quantitative field such as statistics, data science, computer science, economics, or operations research
Experience with causal inference libraries such as CausalML, EconML or DoWhy
Background in deploying models in production settings and working with ML or experimentation infrastructure
Deep understanding of experimentation nuances, including intent-to-treat (ITT) vs. ghost ad methodologies, and the trade-offs between frequentist and Bayesian inference for decision-making under uncertainty
If you have a disability or special need that requires accommodation, please don't be shy and provide us some information.
"Default Together" Policy at Snap: At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better through dynamic collaboration. To reflect this, we practice a "default together" approach and expect our team members to work in an office 4+ days per week.
At Snap, we believe that having a team of diverse backgrounds and voices working together will enable us to create innovative products that improve the way people live and communicate. Snap is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. EOE, including disability/vets.
We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, where applicable).
Our Benefits: Snap Inc. is its own community, so we've got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap's long-term success!
Compensation
In the United States, work locations are assigned a pay zone which determines the salary range for the position. The successful candidate's starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. The starting pay may be negotiable within the salary range for the position. These pay zones may be modified in the future.
Zone A (CA, WA, NYC):
The base salary range for this position is $209,000-$313,000 annually.Zone B:
The base salary range for this position is $199,000-$297,000 annually.Zone C:
The base salary range for this position is $178,000-$266,000 annually.This position is eligible for equity in the form of RSUs.
About Snapchat for Business
Sourced by ZipRecruiter
Industry
Marketing
Company size
1,001 - 5,000 Employees
Headquarters location
Santa Monica, CA, US
Year founded
2011