Machine Learning Engineer salary negotiation
Salary Negotiation
January 17, 2023

The Ultimate Guide to Machine Learning Engineer Salary Negotiation

What are Machine Learning Engineers? Are they well compensated at large tech companies such as Google, Facebook, Netflix, and Apple? What is the average Machine Learning Engineer salary? If you get a Machine Learning Engineer offer, should you negotiate?

The guide below aims to equip you with the essential information you need for your upcoming Machine Learning salary negotiation — based on our experiences across hundreds of Machine Learning Engineer negotiations across Google, Amazon, and more. If you’ve just received your dream Machine Learning job offer, this guide will help you maximize your total compensation.

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

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Table of Contents

What is Machine Learning Engineer?

What does a Machine Learning Engineer do?

Machine learning engineers (MLEs) work on the platforms and infrastructure that facilitate the development, deployment, and monitoring of machine learning models. They might also assist with implementing and scaling Machine Learning models for production or work directly on application-specific Machine Learning systems in a role comparable to an Applied Scientist. Some examples of the platforms that help with the deployment of machine learning models are:

  1. Michelangelo by Uber
  2. Metaflow by Netflix
  3. SageMaker by Amazon
  4. IBM Watson Studio by IBM
  5. Azure Machine Learning Studio by Microsoft

Most machine learning engineers have previously worked as software engineers and have strong expertise in DevOps and software development. Machine Learning Engineers often use more enterprise-oriented and performant programming languages to create systems rather than scientists who utilize Python.

There is a stronger focus on technical design and architecture, infrastructure, scalability, and security. Machine Learning Engineers, who are essentially software engineers with an emphasis on Machine Learning, are the ones that put up the infrastructure and processes around it in contrast to scientists who mostly use technologies.

The table below highlights the differences between Research Engineers, Applied Scientists, and Machine Learning Engineers:

Machine Learning Engineer Research Scientist Applied Scientist
Goal Build infra and platforms for ML capabilities Develop new methodology and techniques Build ML systems to improve business outcomes
Tools Python, Java/Scala, C, Go, Docker, Jenkins, etc Python, deep learning libraries, LaTeX SQL, Hive, Python, ML libraries, Docker, FastAPI, etc.
Skills Software development, DevOps, scalability, security, etc Research, experiments on industry/academic benchmarks, publishing papers Data pipelines, machine/deep learning, experimentation and prototyping, software engineering, DevOps
Deliverables Code for infra and platforms, documentation Papers and code to demonstrate findings Code for ML systems, documents on design, methodology, and experiments

Source: Eugene Yan

Machine Learning Engineer career paths

Since Machine Learning Engineers have a wide variety of expertise, they can branch out into different roles depending on their personal goals and interests. Machine Learning Engineers can pivot into new roles if their current role isn’t as exciting as expected. Some additional career paths for Machine Learning Engineers include:

  • Data Engineer: Data engineers create and maintain the data platforms on which machine learning and AI systems rely. Their primary responsibility is designing information systems for activities including data collection, processing, conversion, mining, and pattern recognition.
  • NLP Scientist: NLP scientists aim to create and build tools and software to recognize human speech patterns and translate spoken words into other languages. The idea is to make it possible for machines to comprehend human languages as people do. A strong command of at least one human language and an understanding of how machines work is prerequisites for this machine-learning vocation.
  • Machine Learning Cloud Architect: The key responsibility of a Machine Learning Cloud Architect involves overseeing an organization's cloud platform. Experience in architecting solutions in AWS and Azure and knowledge of configuration management systems like Chef/Puppet/Ansible are among some of the required skills for cloud architects.

Leading companies to work for as a Machine Learning Engineer.

Some factors to consider when evaluating potential employers include the company's mission and values, the support and resources available to scientists, and the company's culture/work environment. Companies at the cusp of technological innovation and working on cutting-edge projects will have some of the best resources and opportunities for Machine Learning Engineers.

Here are a few examples of companies that are often considered to be among the best places to work for Machine Learning Engineers:

  • Google
  • Facebook
  • Microsoft
  • Amazon
  • Netflix
  • DataBricks
  • IBM
  • Prolifics

Machine Learning Engineer Salary Components

Base Salary

Depending on your location, the base salary offered by companies like Apple, Facebook, and Amazon will differ, along with the rest of the compensation components. Most companies compensate Machine Learning Engineers according to the cost of living in their respective location and the going market rate for engineers in the area, so it's essential to be mindful of how your location will affect your pay. If you are not in the Bay Area, NYC or Seattle, your comp will likely be lower than the numbers you find online.

With that being out of the way, the base salary at Amazon and Apple, unsurprisingly, is the component that moves the least when negotiating. The aforementioned companies have a small band for the base salary within each level, so you'll likely only see your base move by a maximum of $20k-$30k.

Annual Bonus

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. Most big-tech companies offer performance bonuses; it is essential to be aware of the annual bonus when negotiating, especially when you have multiple offers. Companies such as Google and Facebook often apply a company multiplier to performance bonuses, which they also do for stock refreshers.

Of course, annual bonuses are non-negotiable, but if negotiating with a company like Amazon that doesn't offer them, you can always factor them into your counteroffer's base salary as your "yearly cash amount.”

Equity

Companies like Facebook and Microsoft follow a standard and linear vesting schedule of 25% yearly (typical initial grants last four years). Although, companies are starting to get very creative (to gain the upper hand) in how they vest RSUs.

At Facebook, Microsoft, Apple, and most other companies, RSUs are subject to a 4-year vesting schedule: 25% vests at the end of the 1st year (sometimes accompanied by a cliff), then 25% in each of the 2nd, 3rd, and 4th years. For example, if you were given a stock grant of 800k at Apple over four years, the equity would vest as follows:

  • Year 1 - 25% $200k
  • Year 2 - 25% $200k
  • Year 3 - 25% $200k
  • Year 4 - 25% $200k

Google and Amazon do equity vesting a little differently. Google has a front-loaded equity vest (33-33-22-12), while Amazon has a back-loaded equity vest (5-15-40-40). They often use these vesting schedules to inflate your offer and make it look more substantial than it is. In Google’s case, the recruiters often quote the first-year number as your per year total compensation, while Amazon adds a ‘conservative’ 15% growth factor on your future equity. Knowing how the recruiters frame your offer is paramount, as it could be the difference between accepting a great offer and an outstanding one.

Sign-on Bonus

We have seen Machine Learning Engineers get offered a small signing bonus without asking for it - recruiters often use this tactic to sweeten the deal. It’s often possible to increase the signing bonus substantially (this is where having leverage helps!). With the proper leverage and framework, Machine Learning Engineers can get up to $100,000 in Tier 1 locations (Bay Area, New York), which holds across companies like Apple, Google, and more!

Albeit, 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 Apple and Facebook 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.

Recent Top of Band Machine Learning Engineer Offers

L5 (Equivalent) Machine Learning Engineer Base Salary Equity over 4 years ($) Annual Bonus Signing Bonus
Cruise $220,000 $800,000+ 30% up to $100,000
Google $200,000 $650,000+ 15% up to $75,000
Meta $215,000 $750,000+ 15% up to $100,000
DataBricks $210,000 $850,000+ 0% up to $25,000
Apple $200,000 $500,000+ 10% up to $50,000

These are some of the offers we helped negotiate in Tier 1 locations like SF and New York. Although the numbers mentioned above seem very enticing, the proper framework and leverage were used to achieve such strong results. According to our data, Machine Learning Engineers earn about 15-20% more than software engineers on average.

Machine Learning Engineer Salary Negotiation Process

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.

An explicit goal of what a pay increase will help you achieve will help you feel more confident to 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 are certainly within your rights to say, "I don’t feel comfortable sharing that information."
  2. Related - 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 researching market data. I am confident we will get to a number that works for both of us."

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

  1. Research the market: Before beginning negotiations, it's essential to understand the current market for Machine Learning Engineers’ salaries. This will give you a better idea of what to expect regarding salary and benefits and help you understand what other companies offer similar positions. We also have thousands of data points from negotiations we’ve supported and up-to-date data on how the market is trending.
  2. Understand your value: As a Machine Learning Engineer, you bring unique skills and expertise. It's essential to understand the value of your skills and experience and to articulate this value to potential employers during negotiations. Asking your recruiter/hiring manager questions about the scope of the role and responsibilities is a great way to understand how companies like Amazon, Apple, and Microsoft. This will help you negotiate a salary that reflects your worth and the value you can bring to the company. A few great questions to ask are:
    1. What are some of the challenges and opportunities the team is currently facing? How does that translate into initiatives for the team?
    2. What are the main KPIs for the team this year?
    3. What are the major projects the team is working on this year?
  3. Remember you can walk away: Given the long process it often takes to secure a job offer, it can be tempting to sign the first offer you get. However - if negotiations aren’t going well and you’re not excited about the offer you’re receiving, it can be worth considering walking away. Your skills are in demand, and - more than likely - you’re a few weeks away from a better, higher offer that better reflects your value and skills.
  4. See if the company will go above the band: Since the demand for Machine Learning Engineers is high, many companies will go above their standard pay band to get the right candidate to join. We have successfully secured above-band offers from Apple, Facebook, and Amazon. Having another offer or even speaking to another company can create some leverage when making your counteroffer. Additionally, we have seen that patience and slowly moving through the negotiation can help make the company wonder if you’re considering other opportunities.
  5. Ask for specific yearly compensation:We have found that being clear in your request is very important since it communicates confidence and implies that you have deeply thought about your market value. To show flexibility in your ask, you can ask for a total compensation number instead of mentioning a specific breakdown of the base, equity, and signing bonus you want to see. This gives employers such as Meta, Amazon, and Apple a chance to determine how to meet your request using a combination of base, bonus, and equity. However, this is where expertise in negotiating is crucial. Remember to make sure what you’re asking for is above the band to maximize the offer!
  6. Ask for support from the hiring manager: 6. Having a great relationship with your hiring manager is critical for successful negotiation — and, more broadly, ensuring that you’re being set up for success within the company. Suppose your hiring manager is disrespectful or not supportive during the negotiation. How can you expect them to advocate for exciting projects for you to work on or get a promotion in a year or two?

    Often, during negotiations, we help candidates speak with their hiring manager about expectations for the role and the impact they hope to drive. This can help ensure that the hiring manager is 1. someone you want to work with and 2. excited for you to join.

    Once you know you have their support, you’ll have more confidence going back to the recruiter to make an ask for higher compensation.

Machine Learning Engineer Negotiation Tactics

Recruiters commonly use a handful of sneaky tactics to help pull the negotiation in their favor — and it’s essential 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 shortly.

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!
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1:1 Salary Negotiation Support

Negotiation strategy

Step 1 is defining the strategy, which often starts by helping you create leverage for your negotiation (e.g. setting up conversations with FAANG recruiters).

Negotiation anchor number

Step 2 we decide on anchor numbers and target numbers with the goal of securing a top of band offer, based on our internal verified data sets.

Negotiation execution plan

Step 3 we create custom scripts for each of your calls, practice multiple 1:1 mock negotiations, and join your recruiter calls to guide you via chat.

Frequently Asked Questions

Can you negotiate salary after accepting?

It becomes increasingly more challenging to negotiate your salary after accepting the offer. We don’t recommend negotiating salary after you’ve accepted since it might strain your relationship with the Hiring Manager. Furthermore, we haven’t seen much success in negotiating salary after you’ve signed the dotted line.

Are Machine Learning Engineers in demand?

Being an Machine Learning Engineers in tech is prestigious - many large companies are constantly looking for top-level talent. With more and more companies digitizing their data, the demand for scientists will continue to grow.

Will you lose your offer if you negotiate your salary?

A common question that is asked is a valid fear, especially given today’s volatile market conditions. There is always a risk substituted when you negotiate and offer, albeit if negotiation is done with the proper framework, the risk reduces substantially.