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Machine Learning Engineer Search Jobs (NOW HIRING)

Apple

Machine Learning Engineer (Search)

Cupertino, CA · On-site

We are looking for a Machine Learning Engineer to join and play a big part in the next revolution ... the search quality for Apple Maps. Apple Maps and the thousands of applications it empowers are ...

Apple

Machine Learning Engineer (Search)

Cupertino, CA · On-site

$147K - $220K/yr

We are looking for a Machine Learning Engineer to join and play a big part in the next revolution ... to improve the search quality for Apple Maps. Description Apple Maps and the thousands of ...

TikTok

Machine Learning Engineer - Search Ads

San Jose, CA · On-site

$156K - $316K/yr

... Search Ads product from 0 to 1 with a world-class team of passionate engineers. What You'll Do: • ... Machine Learning. • Work on NLP (Natural Language Processing) capability improvement and query ...

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Machine Learning Engineer Search information

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$128.8K

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How much do machine learning engineer search jobs pay per year?

As of Jun 6, 2026, the average yearly pay for machine learning engineer search in the United States is $128,769.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,500.00 and $155,000.00 per year, depending on experience, location, and employer.

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

To thrive as a Machine Learning Engineer, you need strong programming skills (especially in Python), a solid foundation in mathematics and statistics, and a relevant degree in computer science or a related field. Familiarity with machine learning frameworks like TensorFlow or PyTorch, as well as experience using cloud platforms and version control systems, is typically required. Critical thinking, problem-solving, and effective communication skills help set candidates apart in this role. These competencies are crucial for designing, implementing, and optimizing machine learning models that solve real-world business challenges.

How does a Machine Learning Engineer specializing in search typically collaborate with data scientists and product teams?

Machine Learning Engineers working on search functionalities often collaborate closely with data scientists to design, experiment, and refine models that improve search relevance and ranking. They also work with product managers and UX designers to understand user needs and translate them into technical requirements for the search experience. Regular communication ensures that model updates align with business goals and user expectations, and cross-functional meetings are common to review performance metrics and prioritize new features or improvements. This collaborative environment helps drive innovation and ensures the search system meets evolving user demands.

What does a Machine Learning Engineer do?

A Machine Learning Engineer designs, builds, and deploys machine learning models and systems that enable computers to learn from data and make predictions or decisions without being explicitly programmed. They work closely with data scientists to develop algorithms, prepare datasets, and optimize model performance. Additionally, they are responsible for scaling models to production environments, monitoring their outcomes, and maintaining the underlying infrastructure. Their work often involves programming, data preprocessing, model evaluation, and collaboration with cross-functional teams.

What is the difference between Machine Learning Engineer Search vs Data Scientist?

AspectMachine Learning EngineerData Scientist
Required CredentialsBachelor's/Master's in CS, ML, or related fields; experience with ML frameworksBachelor's/Master's in CS, Statistics, or related fields; strong analytical skills
Work EnvironmentDeveloping, deploying, and maintaining ML models in productionAnalyzing data, creating insights, and building predictive models
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, and tech sectors
Search & Comparison IntentFocus on ML model development and deploymentFocus on data analysis and insights generation

While both roles involve working with data and models, Machine Learning Engineers primarily focus on building and deploying scalable ML systems, whereas Data Scientists analyze data to generate insights and inform decision-making. Understanding these differences helps job seekers target the right roles based on their skills and career goals.

More about Machine Learning Engineer Search jobs
What job categories do people searching Machine Learning Engineer Search jobs look for? The top searched job categories for Machine Learning Engineer Search jobs are:
Machine Learning Engineer Search jobs near you

Machine Learning Engineer, Search Quality

ML6

San Francisco, CA • On-site

$150K - $265K/yr

Full-time

Posted 8 days ago

Job description

The Opportunity:
Our client, a category-defining AI platform company based in the San Francisco Bay Area, is seeking a Machine Learning Engineer (Search Quality) to help build the next generation of AI-powered product experiences.
In this role, you'll focus on improving ranking, relevance, and personalization within large-scale search systems. You'll develop new signals, train advanced models, and explore innovative ways of integrating large language models into retrieval systems, directly shaping how users discover and interact with information.
What You'll Be Doing:
  • Develop and introduce novel ranking signals to improve personalization and relevance across the search experience.
  • Train and optimize machine learning models that capture complex interactions within large-scale ranking systems.
  • Design and implement domain-adaptation strategies to tailor language models to specialized data environments.
  • Explore innovative approaches for combining LLMs with search infrastructure to answer nuanced and multi-step queries.
  • Analyze data and run experiments to continuously improve retrieval quality and evaluation metrics.
  • Write clean, maintainable, and well-tested production code.
  • Collaborate closely with cross-functional partners to translate technical improvements into meaningful product impact.
  • Contribute to a strong engineering culture through knowledge sharing and mentorship.

What You'll Need To Be Successful:
  • 2-5 years of professional experience in machine learning or software engineering.
  • Bachelor's degree in Computer Science, Mathematics, Engineering, or a related quantitative field.
  • Hands-on experience working with search systems, recommendation engines, natural language processing, or other large-scale ML-driven systems.
  • Strong analytical skills with comfort working directly with data to improve model performance.
  • Proven ability to design, train, evaluate, and deploy production-ready ML models.
  • Proficiency in an ML framework of your choice (e.g., PyTorch, TensorFlow, etc.).
  • Strong programming skills in languages such as Python, Go, Java, or C++.
  • Ability to thrive in a collaborative, fast-moving environment.
  • Ownership mindset with a proactive approach to experimentation and continuous improvement.

Compensation Range: $150,000 - $265,000 + Equity.

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