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Machine Learning Developer Intern Jobs in Pasadena, CA

Senior Machine Learning Engineer

Los Angeles, CA ยท On-site

$112K - $154K/yr

Experience with cloud platforms, CI/CD, DevOps, orMLOpspractices for deploying and monitoring ... machine learning techniques in real-world systems. "I can apply in less than 4 minutes." You've ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

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Machine Learning Developer Intern information

See Pasadena, CA salary details

$27.8K

$46.5K

$96K

How much do machine learning developer intern jobs pay per year?

As of Jul 13, 2026, the average yearly pay for machine learning developer intern in Pasadena, CA is $46,450.00, according to ZipRecruiter salary data. Most workers in this role earn between $35,500.00 and $50,200.00 per year, depending on experience, location, and employer.

How do Machine Learning Developer Interns typically collaborate with data scientists and engineers during their internship?

Machine Learning Developer Interns often work closely with data scientists to understand the problem domain, gather relevant datasets, and select appropriate models. They also collaborate with software engineers to integrate machine learning solutions into existing systems, ensuring scalability and performance. Regular communication through stand-up meetings, code reviews, and collaborative platforms is common, allowing interns to learn best practices and receive feedback on their work. This teamwork not only enhances technical skills but also provides valuable exposure to real-world deployment and project lifecycle management.

What does a Machine Learning Developer Intern do?

A Machine Learning Developer Intern assists with developing, testing, and implementing machine learning models and algorithms under the guidance of experienced engineers or data scientists. Their tasks may include data preprocessing, model training, evaluating model performance, and helping deploy models into production environments. Interns often collaborate with team members to solve real-world problems using machine learning techniques and may also assist in researching new methodologies or optimizing existing solutions. This role provides hands-on experience in coding, data analysis, and applying theoretical concepts to practical scenarios.

What are the key skills and qualifications needed to thrive as a Machine Learning Developer Intern, and why are they important?

To thrive as a Machine Learning Developer Intern, you need a solid understanding of programming (especially Python), statistics, and machine learning concepts, often supported by coursework or relevant project experience. Familiarity with ML frameworks like TensorFlow or PyTorch, and tools such as Jupyter Notebooks and version control systems like Git, is typically expected. Strong analytical thinking, eagerness to learn, and effective communication help interns contribute to team projects and adapt quickly. These skills are essential for solving real-world problems, collaborating with teams, and building a foundation for a successful career in machine learning.

What is the difference between Machine Learning Developer Intern vs Data Scientist Intern?

AspectMachine Learning Developer InternData Scientist Intern
Required CredentialsTypically pursuing or recently completed a degree in Computer Science, Data Science, or related fields; knowledge of programming languages like Python or JavaSimilar educational background; strong skills in statistics, programming, and data analysis
Work EnvironmentHands-on experience with ML models, algorithms, and software development in tech or research settingsData analysis, visualization, and interpretation in business or research contexts
Employer & Industry UsageTech companies, startups, research labs focusing on AI/ML projectsBusiness, finance, healthcare, and research organizations analyzing large datasets

Both roles involve working with data and programming, but Machine Learning Developer Interns focus more on building and deploying ML models, while Data Scientist Interns emphasize data analysis and insights. The roles often overlap, especially in tech environments, but their core tasks differ slightly.

What cities near Pasadena, CA are hiring for Machine Learning Developer Intern jobs? Cities near Pasadena, CA with the most Machine Learning Developer Intern job openings:
Staff Machine Learning Engineer, Search Ranking

Staff Machine Learning Engineer, Search Ranking

Snapchat

Los Angeles, CA โ€ข On-site

Full-time

Medical

Posted 26 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 Staff Machine Learning Engineer to join Snap Inc! We are looking for a Staff Machine Learning Engineer to lead the development of next-generation Search ranking systems. In this role, you will design, build, and improve machine learning models that determine the relevance, quality, personalization, and utility of search results at scale.

What You'll Do

  • Lead the design and development of machine learning models for Search ranking, including relevance ranking, personalization, result quality, intent understanding, and engagement optimization

  • Own major ranking initiatives from problem definition through experimentation, launch, and iteration

  • Develop and improve ranking models using techniques such as learning-to-rank, deep retrieval, neural ranking, sequence models, embeddings, multi-task learning, calibrated prediction, and large-scale feature engineering

  • Build ranking systems that balance multiple objectives, such as relevance, user satisfaction, freshness, diversity, fairness, safety, latency, and business goals

  • Partner with product managers, data scientists, and engineers to define success metrics, experimentation strategy, and long-term ranking roadmap

  • Analyze user behavior, search logs, query-result interactions, and model performance to identify opportunities for improvement

  • Design robust offline evaluation, online experimentation, and model monitoring frameworks

  • Improve feature pipelines, training infrastructure, serving systems, and model iteration velocity

  • Provide technical leadership across teams, influence architecture decisions, and mentor engineers working on ML ranking systems

  • Stay current with advances in search, recommendation systems, ads ranking, generative AI, LLM-based ranking, and retrieval-augmented systems

Knowledge, Skills, & Abilities

  • Strong machine learning fundamentals, including supervised learning, ranking models, embeddings, deep learning, optimization, evaluation, and experimentation

  • Strong programming skills in Python, C++, Java, Scala, or similar languages

  • Experience with large-scale data processing and ML infrastructure, such as Spark, Flink, Beam, TensorFlow, PyTorch, JAX, or similar tools

  • Ability to take ML models from research or prototyping into large-scale production systems

  • Strong understanding of online experimentation, A/B testing, metric design, model debugging, and tradeoff analysis

  • Proven ability to lead complex technical projects across multiple teams

  • Excellent communication skills and ability to explain complex ML concepts to technical and non-technical stakeholders

Minimum Qualifications

  • Bachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience

  • 8+ years of post-Bachelor's machine learning experience; or Master's degree in a technical field + 7+ year of post-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, personalization, intent understanding, and/or engagement optimization

  • Experience with large-scale data processing and ML infrastructure, such as Spark, Flink, Beam, TensorFlow, PyTorch, JAX, or similar tools

Preferred Qualifications

  • Advanced degree in Computer Science, Machine Learning, Statistics, Mathematics, Information Retrieval, or a related field

  • Direct experience building Search ranking systems, including query understanding, retrieval, ranking, re-ranking, relevance modeling, or result blending

  • Experience with ads ranking, recommendation ranking, feed ranking, marketplace ranking, or content discovery systems

  • Experience with learning-to-rank methods such as LambdaMART, pairwise/listwise ranking losses, neural ranking models, or transformer-based rankers

  • Experience with candidate generation, retrieval models, ANN search, embeddings, vector search, or two-stage ranking architectures

  • Experience optimizing ranking systems for multiple objectives, including relevance, engagement, quality, diversity, freshness, long-term user value, and monetization

  • Experience with LLMs, foundation models, semantic search, natural language understanding, or retrieval-augmented generation

  • Experience building low-latency ML serving systems and improving production model reliability

  • Track record of publishing, patenting, or otherwise advancing the state of the art in search, ranking, recommendations, ads, or applied ML

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 $229,000-$343,000 annually.


Zone B:

The base salary range for this position is $218,000-$326,000 annually.

Zone C:

The base salary range for this position is $195,000-$292,000 annually.This position is eligible for equity in the form of RSUs.