Director of Machine Learning Salary in United States during 2024

Salary details

The average Director of Machine Learning salary lies between USD 181,000 and USD 250,000 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: Dec. 22, 2024

Top 20 Job Tags for Director of Machine Learning roles

The three most common job tag items assiciated with Director of Machine Learning job listings are Machine Learning, Engineering and Research. 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 | 20 jobs Engineering | 17 jobs Research | 14 jobs Computer Science | 14 jobs ML models | 12 jobs Generative AI | 11 jobs PyTorch | 10 jobs AWS | 10 jobs Python | 9 jobs Deep Learning | 8 jobs Architecture | 8 jobs PhD | 8 jobs Statistics | 8 jobs TensorFlow | 7 jobs Testing | 7 jobs Agile | 7 jobs NLP | 6 jobs Spark | 6 jobs Kubernetes | 6 jobs GCP | 6 jobs

Top 20 Job Perks/Benefits for Director of Machine Learning roles

The three most common job benefits and perks assiciated with Director of Machine Learning 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 2024 and the number of open jobs that where offering them during that period:

Career development | 18 jobs Health care | 12 jobs Equity / stock options | 11 jobs Competitive pay | 10 jobs Wellness | 7 jobs Insurance | 7 jobs Flex hours | 6 jobs Startup environment | 6 jobs Salary bonus | 6 jobs Flex vacation | 5 jobs 401(k) matching | 3 jobs Parental leave | 3 jobs Transparency | 2 jobs Relocation support | 2 jobs Medical leave | 2 jobs Gear | 1 jobs Team events | 1 jobs Home office stipend | 1 jobs Flexible spending account | 1 jobs Unlimited paid time off | 1 jobs

Salary Composition for a Director of Machine Learning

The salary for a Director of Machine Learning typically comprises a base salary, performance bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The base salary often constitutes the largest portion, ranging from 60% to 80% of the total compensation package. Performance bonuses can vary significantly, often between 10% to 20%, depending on the company's performance and individual achievements. Additional remuneration, such as stock options, can be a significant part of the package, particularly in startups or large tech firms, and may account for 10% to 30% of the total compensation.

Regional differences also play a role; for instance, salaries in tech hubs like Silicon Valley or New York City tend to be higher due to the cost of living and competitive job market. Industry variations are notable as well, with finance and tech industries typically offering higher compensation compared to academia or non-profit sectors. Company size can influence salary composition, with larger companies often providing more substantial bonuses and stock options.

Steps to Increase Salary from This Position

To increase your salary from the position of Director of Machine Learning, consider the following strategies:

• Expand Your Skill Set: Continuously update your technical skills and knowledge of the latest AI/ML trends and technologies. This can make you more valuable to your current employer or attractive to potential employers.

• Pursue Leadership Opportunities: Take on more significant leadership roles within your organization, such as leading larger teams or spearheading critical projects. Demonstrating your ability to drive business results can justify a higher salary.

• Network and Build Industry Connections: Engage with industry peers through conferences, seminars, and online platforms. Networking can open doors to higher-paying opportunities and provide insights into industry salary standards.

• Negotiate Effectively: When discussing salary, be prepared with data on industry standards and your contributions to the company. Highlight your achievements and the value you bring to the organization.

• Consider Relocation: If feasible, relocating to a region with higher salary standards for your role can lead to a significant pay increase.

Educational Requirements

For a Director of Machine Learning, a strong educational background is essential. Most candidates hold at least a master's degree in computer science, data science, machine learning, or a related field. A Ph.D. is often preferred, especially in research-intensive roles or companies that prioritize cutting-edge innovation. This advanced education provides a deep understanding of machine learning algorithms, data analysis, and statistical modeling, which are crucial for the role.

Helpful Certifications

While not always mandatory, certain certifications can enhance your credentials and demonstrate your expertise in AI/ML. Some valuable certifications include:

• Certified Machine Learning Professional (CMLP)
• AWS Certified Machine Learning – Specialty
• Google Professional Machine Learning Engineer
• Microsoft Certified: Azure AI Engineer Associate

These certifications can validate your skills in specific platforms and tools, making you more competitive in the job market.

Required Experience

Typically, a Director of Machine Learning is expected to have extensive experience in the field, often 10+ years, with a significant portion in leadership roles. This experience should include hands-on work with machine learning models, data analysis, and software development. Experience in managing teams, developing AI strategies, and implementing machine learning solutions in a business context is crucial. Additionally, experience in a specific industry can be beneficial, as it provides domain knowledge that can be applied to AI/ML projects.

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