Compensation Sheet

We have a set system for compensation as part of being transparent.

You can use formula below to see what your compensation might look like when you’re joining TabbyML, and to see how it might develop over time.

Role

$185,000 Software Engineer
$225,000 Machine Learning Engineer
$134,000 DevOps Engineer

Location

United States

1.0 San Francisco, California
0.9 New York, New York
0.9 Seattle, Washington
Texas

0.8 Dallas / Austin
0.75 Houston
0.74 San Antonio
0.65 Everywhere else

0.7 Hawaii
Massachusetts

0.85 Boston
0.74 Everywhere else

German

0.65 Munich/Nuremberg
0.65 Frankfurt/Stuttgart
0.65 Hamburg/Bremen
0.65 Berlin/Leipzig
0.6 Everywhere else

China

0.6 All

United Kingdom

0.67 London, England
0.6 Everywhere else, England

Level

0.59 Junior
0.78 Intermediate
1 Senior
1.2 Staff

Step

0.85 - 0.94 Learning
0.95 - 1.04 Established
1.05 - 1.1 Thriving
1.11 - 1.2 Expert

Level

More experience does not correlate with increased importance. Seniority is not a title - we don't believe in having a huge hierarchy of roles, as everyone needs to feel like the owner of the company that they are.

We pay more experienced team members a greater amount since it is reasonable to expect this correlates with an increase in skill - being able to ship faster through less time having to work things out for the first time is valuable. Experienced hires can help upskill the less experienced hires on the team too. Team members who have less experience can see a steady increase in pay over time as they increase their experience and skill.

We believe at first increased skill comes from more time spent in the role. Over time, this judgement becomes more subjective and is instead based on the speed with which you can ship or help the team to ship, the quality of your prioritization and decision-making, as well as your technical approach.

Step

Within each level, we believe there's a place to have incremental steps to allow for more flexibility. We define these as follows:

Learning: Starting to match expectations.

Established: Matching expectations.

Thriving: Exceeding expectations.

Expert: Exceeding expectations consistently.

With exception of team members at the very beginning of their career, we hire into the Established step by default. This will give everyone the opportunity to be set up for success and leave enough room for salary increases, without the need to move up in seniority.

The step factor is adjusted quarterly. For example, an employee will have a factor of Established for their first quarter. At the end of the first quarter, the employee will receive a new rating and factor for their Step, which will be applied to their second quarter.

Location

Most of our location factors are based on GitLab's location factors, and are based on market rates, not cost of living. GitLab uses a combination of data from Economic Research Institute (ERI), Numbeo, Comptryx, Radford, Robert Half, and Dice to calculate what a fair market rate is for each location. Read more on how GitLab calculates this location factor.

In order to simplify location factors, we have added a floor at 0.6 globally, and 0.65 specifically for the US. This means nobody will have a location factor lower than 0.6. We are aware that this might lead to comparatively low number for certain areas in comparison to others, but this approach allows us to move fast now and adjust the location data later on, if needed.

If your location isn't listed, we will create one for you based on Numbeo's data for the relative Cost Of Living with San Francisco.

Equity

It’s important to us that all TabbyML employees can feel invested in the company’s success. Every one of us plays a critical role in the business and deserves a share in the companies success as we grow. When employees perform well, they contribute to the business doing well, and therefore should share a part of the increased financial value of the business.

As part of your compensation, you will receive share options in the company. Broadly, the amount of options will depend on the Level as per the Experience Factor. We may change this policy from time to time depending on our rate of hiring - e.g. if we had a gap in hiring for an extended period, we would adjust this.

Whilst the terms of options for any company could vary if we were ever acquired, we have set them up with the following key terms which we believe are industry-leading in their friendliness to employees:

Standard 4-year vesting with a 1-year cliff

10 years to exercise your options in the event that you leave TabbyML

Double trigger acceleration, which means if you are let go or forced to leave due to the company being acquired, you receive all of your options at that time