Salary for Senior-level / Expert Machine Learning Engineer in United States during 2023
💰 The median Salary for Senior-level / Expert Machine Learning Engineer in United States during 2023 is USD 197,601
✏️ This salary info is based on 838 individual salaries reported during 2023
Salary details
The average senior-level / expert Machine Learning Engineer salary lies between USD 152,000 and USD 238,000 in the United States. It represents the overall compensation/gross salary amount for the working year (before deductions like social security, taxes and other contributions), not including equity/stock options or similar benefits.
- Job title
- Machine Learning Engineer
- Experience
- Senior-level / Expert
- Region
- United States
- Salary year
- 2023
- Sample size
- 838
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- Top 25%
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- Median
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- Bottom 25%
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Region represents the primary country of residence of an employee during the year (or residence for tax purposes). All data shown are full-time equivalent (FTE) salaries. Part-time salary information has been extrapolated to its FTE value.
Last updated:Salary trend
Top 20 Job Tags for Senior-level / Expert Machine Learning Engineer roles
The three most common job tag items assiciated with senior-level / expert Machine Learning Engineer job listings are Machine Learning, Python and Engineering. Below you find a list of the 20 most occuring job tags in 2023 and the number of open jobs that where associated with them during that period:
Machine Learning | 1268 jobs Python | 1050 jobs Engineering | 998 jobs ML models | 747 jobs Computer Science | 735 jobs PyTorch | 611 jobs TensorFlow | 554 jobs Deep Learning | 528 jobs Research | 510 jobs Pipelines | 508 jobs AWS | 452 jobs NLP | 431 jobs Testing | 398 jobs Statistics | 398 jobs Architecture | 378 jobs Java | 361 jobs Spark | 322 jobs Mathematics | 317 jobs SQL | 311 jobs PhD | 306 jobsTop 20 Job Perks/Benefits for Senior-level / Expert Machine Learning Engineer roles
The three most common job benefits and perks assiciated with senior-level / expert Machine Learning Engineer job listings are Career development, Health care and Equity / stock options. Below you find a list of the 20 most occuring job perks or benefits in 2023 and the number of open jobs that where offering them during that period:
Career development | 1072 jobs Health care | 562 jobs Equity / stock options | 499 jobs Flex hours | 465 jobs Flex vacation | 407 jobs Salary bonus | 390 jobs Startup environment | 345 jobs Parental leave | 321 jobs Competitive pay | 283 jobs Medical leave | 268 jobs Insurance | 267 jobs Team events | 250 jobs Wellness | 209 jobs 401(k) matching | 160 jobs Home office stipend | 149 jobs Unlimited paid time off | 82 jobs Signing bonus | 74 jobs Flexible spending account | 72 jobs Conferences | 64 jobs Relocation support | 61 jobsSalary Composition for Senior-Level Machine Learning Engineers
The salary for a Senior-level or Expert Machine Learning Engineer in the United States typically comprises several components: a fixed base salary, performance-based bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The base salary is the most substantial part, often accounting for 70-80% of the total compensation package. Bonuses can vary significantly depending on the company's performance and individual contributions, usually ranging from 10-20% of the base salary. Additional remuneration, such as stock options, can be a significant part of the package, particularly in startups or large tech firms, and can vary widely based on the company's valuation and growth prospects.
Regional differences also play a role; for instance, salaries in tech hubs like San Francisco or New York City tend to be higher due to the cost of living and competitive job markets. Industry-wise, sectors like finance, healthcare, and technology often offer higher compensation compared to academia or non-profit organizations. Company size can also influence salary composition, with larger companies typically offering more comprehensive benefits and stock options.
Steps to Increase Salary Further
To increase your salary beyond the median for a Senior-level Machine Learning Engineer, consider the following strategies:
- Specialize in High-Demand Areas: Focus on niche areas within AI/ML, such as deep learning, natural language processing, or computer vision, which are in high demand and can command higher salaries.
- Leadership Roles: Transition into leadership or managerial roles, such as a Machine Learning Team Lead or Director of AI, which typically offer higher compensation.
- Continuous Learning: Stay updated with the latest technologies and methodologies in AI/ML. Pursuing advanced certifications or courses can enhance your expertise and marketability.
- Networking and Industry Engagement: Engage with professional networks, attend conferences, and contribute to open-source projects to increase your visibility and opportunities for higher-paying roles.
- Negotiation Skills: Improve your negotiation skills to better advocate for higher compensation during job offers or performance reviews.
Educational Requirements
Most Senior-level Machine Learning Engineer positions require at least a Master's degree in a relevant field such as Computer Science, Data Science, Statistics, or Electrical Engineering. A Ph.D. is often preferred, especially for roles involving research and development of new algorithms or technologies. The educational background should include a strong foundation in mathematics, statistics, and computer programming.
Helpful Certifications
While not always mandatory, certain certifications can enhance your credentials and demonstrate expertise in specific areas of AI/ML. Some valuable certifications include:
- Google Professional Machine Learning Engineer
- AWS Certified Machine Learning – Specialty
- Microsoft Certified: Azure AI Engineer Associate
- TensorFlow Developer Certificate
These certifications can validate your skills and knowledge, making you a more attractive candidate for senior roles.
Required Experience
Typically, a Senior-level Machine Learning Engineer is expected to have at least 5-10 years of experience in the field. This experience should include hands-on work with machine learning models, data analysis, and software development. Experience in leading projects, mentoring junior engineers, and collaborating with cross-functional teams is also highly valued.
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