Salary for Senior-level / Expert Lead Engineer in United States during 2024

Salary details

The average senior-level / expert Lead Engineer salary lies between USD 154,000 and USD 231,000 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

Top 20 Job Tags for Senior-level / Expert Lead Engineer roles

The three most common job tag items assiciated with senior-level / expert Lead Engineer job listings are Engineering, Python and Machine 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:

Engineering | 135 jobs Python | 128 jobs Machine Learning | 110 jobs Computer Science | 92 jobs Security | 76 jobs Architecture | 63 jobs Java | 59 jobs AWS | 57 jobs Testing | 56 jobs Privacy | 56 jobs Research | 54 jobs APIs | 49 jobs SQL | 48 jobs Big Data | 47 jobs Azure | 47 jobs Generative AI | 46 jobs Pipelines | 45 jobs LLMs | 41 jobs Agile | 39 jobs Scala | 35 jobs

Top 20 Job Perks/Benefits for Senior-level / Expert Lead Engineer roles

The three most common job benefits and perks assiciated with senior-level / expert Lead Engineer job listings are Career development, Competitive pay and Health care. 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 | 133 jobs Competitive pay | 65 jobs Health care | 55 jobs Team events | 47 jobs Flex hours | 39 jobs Startup environment | 28 jobs Insurance | 28 jobs Salary bonus | 27 jobs Flex vacation | 22 jobs Equity / stock options | 18 jobs Medical leave | 13 jobs Wellness | 12 jobs Transparency | 7 jobs Conferences | 6 jobs 401(k) matching | 5 jobs Parental leave | 5 jobs Flat hierarchy | 3 jobs Home office stipend | 3 jobs Fitness / gym | 2 jobs Relocation support | 2 jobs

Salary Composition

In the United States, the salary composition for a Senior-level or Expert Lead Engineer in AI/ML/Data Science typically includes a combination of a fixed base salary, performance-based bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The base salary often constitutes the majority of the total compensation package, ranging from 60% to 80%. Bonuses can vary significantly depending on the company’s performance and individual contributions, usually accounting for 10% to 20% of the total compensation. Additional remuneration, such as stock options, can be a significant part of the package, particularly in startups or large tech firms, and may range from 10% to 30%.

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, tech companies, finance, and healthcare often offer higher compensation compared to academia or non-profit sectors. Company size can also influence salary composition, with larger companies typically offering more comprehensive benefits and stock options.

Increasing Salary Further

To increase your salary further from this position, consider the following strategies:

• Specialization: Develop expertise in a niche area of AI/ML that is in high demand, such as deep learning, natural language processing, or AI ethics.
• Leadership Roles: Transition into managerial or executive roles, such as Director of AI or Chief Data Scientist, which often come with higher compensation.
• Continuous Learning: Stay updated with the latest technologies and methodologies in AI/ML through courses, workshops, and conferences.
• Networking: Build a strong professional network to learn about new opportunities and gain insights into industry trends.
• Consulting: Consider offering consulting services or starting your own venture, which can provide additional income streams.

Educational Requirements

Most senior-level positions in AI/ML/Data Science require at least a master’s degree in a related field such as Computer Science, Data Science, Statistics, or Mathematics. A Ph.D. is often preferred, especially for roles that involve research and development of new algorithms or technologies. Strong foundational knowledge in machine learning, data analysis, and programming is essential.

Helpful Certificates

While not always mandatory, certain certifications can enhance your credentials and demonstrate expertise:

• Certified Machine Learning Professional (CMLP)
• TensorFlow Developer Certificate
• AWS Certified Machine Learning – Specialty
• Microsoft Certified: Azure AI Engineer Associate
• Data Science Professional Certificate by IBM

These certifications can validate your skills and knowledge, making you a more attractive candidate to potential employers.

Required Experience

Typically, a senior-level position requires at least 7-10 years of experience in the field, with a proven track record of leading projects and teams. Experience in deploying machine learning models in production, working with large datasets, and using AI/ML tools and frameworks is crucial. Demonstrated success in solving complex problems and delivering business value through data-driven solutions is highly valued.

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