Machine Learning Researcher Salary in United States during 2024

Salary details

The average Machine Learning Researcher salary lies between USD 110,000 and USD 160,000 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.

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: Nov. 17, 2024

Salary trend

Top 20 Job Tags for Machine Learning Researcher roles

The three most common job tag items assiciated with Machine Learning Researcher job listings are Machine Learning, Python and Deep Learning. Below you find a list of the 20 most occuring job tags in 2024 and the number of open jobs that where associated with them during that period:

Machine Learning | 357 jobs Python | 330 jobs Deep Learning | 325 jobs Engineering | 322 jobs Statistics | 311 jobs LLMs | 304 jobs ML models | 303 jobs Testing | 290 jobs MLOps | 284 jobs Git | 284 jobs AWS | 283 jobs SQL | 278 jobs GCP | 278 jobs Kubernetes | 277 jobs Docker | 277 jobs Data visualization | 276 jobs Looker | 274 jobs Business Intelligence | 274 jobs Research | 79 jobs Computer Science | 59 jobs

Top 20 Job Perks/Benefits for Machine Learning Researcher roles

The three most common job benefits and perks assiciated with Machine Learning Researcher job listings are Career development, Startup environment and Equity / stock options. Below you find a list of the 20 most occuring job perks or benefits in 2024 and the number of open jobs that where offering them during that period:

Career development | 341 jobs Startup environment | 291 jobs Equity / stock options | 202 jobs Competitive pay | 189 jobs Salary bonus | 184 jobs Conferences | 44 jobs Health care | 37 jobs Flex vacation | 16 jobs Relocation support | 16 jobs Team events | 14 jobs Flex hours | 12 jobs Medical leave | 9 jobs Parental leave | 8 jobs Insurance | 8 jobs 401(k) matching | 7 jobs Wellness | 7 jobs Transparency | 4 jobs Unlimited paid time off | 3 jobs Lunch / meals | 1 jobs Fitness / gym | 1 jobs

Salary Composition for Machine Learning Researchers

The salary for a Machine Learning Researcher 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 significant portion, often accounting for 70-80% of the total compensation package. Bonuses can vary widely, ranging from 10-20% of the base salary, depending on individual and company performance. Additional remuneration, such as stock options, is more common in larger tech companies and startups, where it can form a substantial part of the total compensation, sometimes equaling or exceeding the base salary over time.

Regional differences also play a role. For instance, salaries in tech hubs like San Francisco, New York, and Seattle tend to be higher due to the cost of living and competition for talent. Industry-wise, tech companies, financial services, and healthcare often offer higher salaries compared to academia or government roles. Company size can also influence salary composition, with larger companies typically offering more comprehensive benefits and stock options.

Steps to Increase Salary

To increase your salary beyond the median of USD 160,000, 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.
• Pursue Leadership Roles: Transitioning into a lead or managerial position can significantly boost your salary. This may involve overseeing a team of researchers or leading projects.
• Enhance Your Skills: Continuously update your skills with the latest tools and technologies in AI/ML. This could involve learning new programming languages, frameworks, or methodologies.
• Network and Build a Personal Brand: Attend conferences, publish research papers, and engage with the AI/ML community to build your reputation and open up new opportunities.
• Negotiate Offers: When changing jobs or during performance reviews, negotiate your salary and benefits package. Use industry benchmarks to justify your requests.

Educational Requirements

Most Machine Learning Researcher positions require at least a master's degree in a related field such as computer science, data science, mathematics, or statistics. A Ph.D. is often preferred, especially for research-intensive roles, as it demonstrates a deep understanding of complex algorithms and the ability to conduct independent research. Candidates with a strong academic background in machine learning, artificial intelligence, or a related discipline are highly sought after.

Helpful Certifications

While not always mandatory, certain certifications can enhance your profile and demonstrate your expertise in AI/ML:

• TensorFlow Developer Certificate: Validates your ability to build and train machine learning models using TensorFlow.
• AWS Certified Machine Learning – Specialty: Demonstrates your skills in designing, implementing, and maintaining machine learning solutions on the AWS platform.
• Microsoft Certified: Azure AI Engineer Associate: Shows proficiency in using Azure AI services to build and integrate AI solutions.
• Google Professional Machine Learning Engineer: Certifies your ability to design, build, and productionize ML models on Google Cloud.

Experience Requirements

Typically, employers look for candidates with at least 2-5 years of experience in machine learning or a related field. This experience should include hands-on work with machine learning models, data analysis, and programming. Experience in research, whether in academia or industry, is highly valued, as it demonstrates the ability to tackle complex problems and contribute to innovative solutions.

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