OpenAI, DeepMind, C3AI, & IBM: AI/ML Salaries & Negotiations

Artificial Intelligence/Machine Learning (which we’ll refer to as AI/ML) is a rapidly growing field — with tons of demand for talented engineers. How do compensation and career progression look at an Artificial Intelligence or Machine Learning startup vs. a FAANG company? Are compensation packages in AI/ML structured the same as other types of engineering roles?

This guide aims to equip you with the essential information you need your upcoming Machine Learning salary negotiation — based on our experience across hundreds of Research Scientist, Applied Scientist, and ML Engineer negotiations. If you’ve just received your dream ML job offer, the article below will help you maximize your total compensation.

If your situation is unique or you want 1:1 support, please sign up for a free consultation with one of our expert negotiators.

Table of Contents

Technical Roles

Before talking about compensation and the negotiation process in the AI/ML industry, it’s important to briefly level-set which roles Machine Learning companies tend to recruit for. The most prominent players in the space - such as C3ai, DeepMind, Facebook (Meta), Google, IBM, and OpenAI - most commonly hire for these three roles:

1. Machine Learning Engineer: Responsible for designing, building, and deploying machine learning models and systems. ML Engineers may also be involved in managing the infrastructure and resources needed to support the machine learning process, as well as the deployment and maintenance of machine learning systems in production environments.
2. Applied Scientist: Often works closely with domain experts to understand the problem or challenge the organization is looking for AI/ML to address and determine the best solution. Applied Scientists may also communicate their work results to stakeholders, including technical and non-technical audiences.
3. Research Scientist: Investigate and conduct original research in machine learning. They use data mining, statistical analysis, and machine learning algorithms to explore and understand complex datasets, identify patterns and relationships within the data, and generate new insights and knowledge.

The recruiting and negotiation process is quite similar across all three roles so - in this article - we will use the term Machine Learning Engineer interchangeably with Applied Scientist and Research Scientist for simplicities sake.

Compensation Structure for Artificial Intelligence/Machine Learning Roles

Base Salary and Bonus

Like most other roles, base salaries for ML roles can vary depending on various factors, such as size, location, and industry. However, overall, Machine Learning roles tend to command high salaries due to the specialized nature of the work and the high demand for skilled professionals in this field.

For example, an ML engineer at Facebook can make around 5-7% more than a software engineer at the same level (keeping other factors constant).

Annual bonuses are another component that can be coupled with the base salary. The annual bonus is usually non-negotiable and highly subjective to the company you are applying to. For example, Google has a baseline 15% annual performance bonus for an L5 ML Engineer, while Amazon will not have an annual performance bonus for an ML role at the same level.

Equity

Working in AI means you’ll likely be at a tech company — either a startup or a more established firm — where equity will be a meaningful part of your compensation.

The RSU bands are wide at most prominent tech companies for Machine Learning Engineers; again, they pay higher than most other roles from the get-go.

Most large-cap tech companies have a traditional vesting structure with an initial equity grant that vests in equal installments over four years.

As an example - if you are granted $800K in RSUs, you will receive:

• Year 1: 25% ($200K)
• Year 2: 25% ($200K)
• Year 3: 25% ($200K)
• Year 4: 25% ($200K)

Signing Bonus

We often see Machine Learning offers include a signing bonus from the get-go to sweeten the deal. And - it’s often possible to increase the signing bonus substantially (this is where having leverage helps!). For example, the top-of-band sign-on bonus for an L5 Machine Learning Engineer at Facebook in a Tier 1 location (Bay Area, New York) is $100,000.

However, not all Machine Learning offers include a signing bonus by default. It's a common recruiter trick to leave a signing bonus out of the initial offer so they can add it as part of the negotiation (and avoid increasing the base or equity, which could require more senior-level approval). Some recruiters have claimed that the company does not offer sign-on bonuses, which isn’t always the case. Recruiters at DeepMind and OpenAI will not initially include a sign-on bonus. Instead, a sign-on bonus is often added when specific leverage/information is shared with them.

Most companies will reserve the right to “claw back” a portion of your signing bonus if you leave before the 1-year mark. This is normal for major tech companies, commonly only requiring you to repay the pro-rata amount — for example, if you leave after ten months, you would need to pay 2/12 of your signing bonus back.

Above-Band Offers

Most companies have the flexibility to give above-band compensation for senior roles, but this often requires a complex and lengthy negotiation. At Rora, we’ve successfully negotiated multiple above-band offers. In a few situations, the candidate needed executive approval to increase the offer. For example, the top band total compensation (base salary + annual bonus + signing bonus + equity) for an L5 Machine Learning Engineer at Facebook in the Bay Area is ~$441k per year; however, for one candidate, we were able to successfully negotiate a total compensation of ~$503k per year.

Differences between ML and Software Engineer Negotiations

Technical Differences

While Software and Machine Learning Engineers are both involved in developing and implementing technology solutions, some critical differences exist in the specific tasks and skills for each role.

One of the main differences is the focus of each field. Software Engineers primarily design, develop, and maintain software systems and applications. This can include writing code, testing and debugging software, and working with teams to design and implement software solutions.

On the other hand, ML Engineers focus on developing and implementing machine learning models and algorithms. This can involve training and fine-tuning machine learning models, working with large datasets, and deploying machine learning systems in production environments. Machine Learning Engineers often have strong computer science and mathematics backgrounds and experience with machine learning frameworks and libraries.

Another key difference is the tools and technologies used in each field. Depending on the project's specific requirements, software engineers typically work with various programming languages and tools. This can include languages such as Java, Python, and C++ and tools for version control, debugging, and testing.

On the other hand, Machine Learning Engineers often work with specialized tools and technologies designed for machine learning. This can include machine learning frameworks such as TensorFlow and PyTorch and tools for working with large datasets and deploying machine learning models in production environments. Machine learning engineers may also use languages such as Python and R, which strongly focus on data analysis and scientific computing.

Compensation Differences

While Software Engineering and Machine Learning Engineering are both rewarding careers with plenty of growth opportunities and substantial pay, the high demand and specialized skills required for Machine Learning Engineering can result in significantly higher compensation. According to our data, Machine Learning Engineers earn about 20% more than software engineers.

‍

There are several reasons for this significant difference in compensation:

Demand is Booming

One reason is the high demand for Machine Learning Engineers, as machine learning has started to play a much bigger role in many sectors, from software to manufacturing to retail. Within the tech industry, the bounds of machine learning are continually being stretched.

As more applications need real-time or almost real-time conclusions, the complexity of machine learning models and systems engineering has increased. At the same time, workplace access to 'off-the-shelf' machine learning software has increased. Machine Learning Engineers are in high demand due to both of these processes.

This high demand has increased salaries for these professionals, as companies are willing to pay a premium to attract and retain top talent.

The demand for Machine Learning Engineers is already high and is only anticipated to expand as machine learning becomes more advanced and widely available.

This growth also impacts the future of roles outside of Machine Learning Engineers. Mike Roberts, Vice President of AI and Machine Learning at Hypergiant, an enterprise AI startup, has mentioned that the demand for Data Scientists increases as Machine Learning complexity rises (BuiltIn).

According to the Bureau of Labor Statistics, this is precisely the case, which estimates that demand for Data Scientists would see 22% growth by 2030. Although Data Scientists don’t get compensated the same as Machine Learning engineers, we expect to see an uptick in Data Scientist compensation as demand increases.

Educational Requirements

Another factor to consider is the level of education and experience required for each field. Machine Learning Engineers often have more advanced degrees, such as a Master's or Ph.D. in machine learning or a related field. This higher level of education can also lead to higher pay — as there’s a higher bar required to get into the field.

Compensation Varies by Industry

Additionally, the specific industry in which a Software Engineer or Machine Learning Engineer works can also impact their salary. For example, Machine Learning Engineers in the finance or healthcare industries may earn higher salaries due to their work's sensitive and complex nature.

Negotiating a Machine Learning Job Offer

Before preparing for a negotiation, make sure you have a good understanding of both your financial and career goals. This will help you decide what you should be asking for and make you better prepared to negotiate effectively.

For example, if you hope to save a certain amount of money each month or save for a specific goal, you should make sure the salary you are negotiating will help you get there. This may mean you ask for more salary instead of a non-cash benefit like equity.

Having a clear goal of what an increase in pay will help you achieve will help you feel more confident to make your ask.

If you haven’t yet received an offer, here are a few things to consider during the interview process:

1. Do not share your current compensation. In many states (e.g., California), it is illegal for companies to ask for this. If a recruiter asks you, you can say, "I don’t feel comfortable sharing that information."
2. We do not recommend sharing your compensation expectations before receiving an offer. Most companies will pay very competitively and will be willing to negotiate after giving an initial offer. If you choose to throw out a high number when asked, that will increase the chance you are required to provide proof of a competing offer. Instead, if you’re asked for your pay expectations, we recommend you reply with, "I'm focused on the interview process and still doing my research on market data. I am very confident that we will be able to get to a number that works for both of us."

Negotiating a salary, equity, and signing bonus for a Machine Learning position can be a daunting task. Still, with the proper knowledge and preparation, you can increase your chances of securing a fair and competitive offer. We recommend you:

Negotiation Framework: An Ishikawa diagram helps us understand our current leverage and effectively visualizes complex problems such as negotiating compensation.

‍

AI/ML Negotiation Tactics

Employers commonly use a handful of sneaky tactics to help pull the negotiation in their favor — and it’s important to be aware of them to avoid being taken advantage of. The most common tactics include putting time pressure on you with an exploding deadline, mentioning that the initial offer is non-negotiable (even though it is!), selling you on company growth and saying that your equity value will increase substantially, and promising to revisit pay in the near future.

Some of the most common negotiation strategies that we use in rebuttal are:

• Putting pressure on the employer – Employers are often pressured to fill a position quickly. You can use this to your advantage by pressing them to offer you a higher salary by saying “I know you’re trying to wrap up the negotiation - here’s what I’d need to sign.”
• Standing in a stronger position – If you have a competing job offer (or are currently employed), you are in a stronger negotiating position. You can use this to your advantage by asking what you want regarding salary, benefits, and equity.
• Sharing outside information – You can also use outside information to strengthen your position. For example, you could talk with a competitor about the compensation they might be offering. Recruiters can be great resources for this information, too!

As the AI/ML industry grows we’ll see more and more companies look to recruit ML Engineers. Understanding how ML Engineer compensation typically works — and negotiating the offers you receive is incredibly important — not just to ensure you’re earning competitive pay, but also to ensure you understand the dynamics of the company you’re joining and are being set up for success within the role.