Skip to main content
GullyHR

Information Technology and Software · Machine learning companies

HR for Machine Learning Companies: Data Scientists, ML Engineers and Research Pace

A machine learning company builds models and systems that learn from data. Here is how to manage data scientists, ML engineers and annotators where research is uncertain, compute is costly and good people are very scarce.

Share your requirementsSee the HR topics
A developer discussing career progression with an engineering manager and HR partner
Recognise the pressureWhere people processes need attentionBuild your HR set-upPractical steps for your businessChoose your first stepCheck what is working today

Not sure what you need? Tell us about your business in six short steps and we will come to the first meeting prepared.

Share your requirements

A machine learning company builds models that predict, classify, recommend or generate. Teams include data scientists who experiment, ML engineers who put models into production, data engineers, annotators who label data, product managers and researchers. Progress is uncertain. An experiment may take weeks and fail, which is hard to explain to clients and investors. Compute is costly, so teams fight for it. Good data scientists are few and often have strong opinions about method. Annotators do repetitive work, and their quality decides the model, yet they are often paid and treated as the lowest tier. Ethical questions about data, bias and use arise, and staff want to be heard. See also HR for IT and software.

  1. Plan
  2. Hire
  3. Onboard
  4. Manage
  5. Develop
  6. Perform
  7. Engage
  8. Retain
  9. Exit
  10. Improve

When HR is weak in machine learning companies, you may see

  • Experiments fail and nobody says so
  • Researchers and engineers clash
  • Annotators are overlooked
  • Compute is a scarce resource
  • Scarce talent is courted constantly
  • Ethics concerns are raised and dropped
  • Clients expect certainty
  • Models need upkeep after launch

These are the places the people side of this kind of business tends to give way. The rest of this page works through them.

Who does the work in machine learning companies

HR here has to work for each of these groups, not only the office. The set-up below is written with all of them in mind.

Roles we help you hire in machine learning companies

GullyHR runs talent acquisition for permanent roles in machine learning companies. For each role we agree the job description with you, find and screen candidates against it, and support the offer through to joining. Tell us the role and the number of people, and we start there.

Job descriptions for this sector

Share a hiring requirementTalent acquisition services →IT and specialised recruitment →Role design and job descriptions →

We recruit permanent employees. We do not supply contract or temporary staff.

We set up the interview and hiring process for machine learning companies

Every business interviews, but few have a written process that works the same way for every role and every interviewer. GullyHR designs yours, from the role brief to the offer, trains your managers to run it, and can run it for you on the roles you hand us.

  1. 1. Role brief and scorecard

    We agree what the role must deliver, the must-have and nice-to-have skills, and how each will be scored.

  2. 2. Stages and sequence

    Screening call, assessment, interview rounds and decision points, sized to the role and the number of people you hire.

  3. 3. Question bank and scoring

    Structured questions for each role family and a rating guide, so two interviewers judge the same answer the same way.

  4. 4. Interviewer readiness

    A short briefing for your managers on what to ask, what to avoid and how to give a clear yes or no.

  5. 5. Offer and follow-up

    Who approves, how the offer is made, and how candidates are kept informed from first call to joining.

Set up our interview processRecruitment process consulting →Structured interviewing →Interviewer training →Recruitment software →

Where HR strains in machine learning companies

  • Experiments fail and nobody says so

    Teams hide negative results to avoid blame, which wastes time and hides what was learned.

  • Researchers and engineers clash

    Researchers want to explore, engineers want to ship, and ownership of the model blurs.

  • Annotators are overlooked

    Labelling is repetitive, pay is low, and quality problems are blamed on them rather than the instructions.

  • Compute is a scarce resource

    Teams compete for training time, and ranking by seniority breeds resentment.

  • Scarce talent is courted constantly

    Strong data scientists are approached weekly, and counter-offers are expensive.

  • Ethics concerns are raised and dropped

    Staff worry about bias or misuse of data, and have no clear place to say so.

  • Clients expect certainty

    Business buyers expect fixed results from work that is statistical, and the team absorbs the gap.

  • Models need upkeep after launch

    Deployed models drift, and someone must watch them, but that duty is rarely planned.

HR topics for machine learning companies

Start with the topics that matter most here: Performance Management; Retention and Attrition Management; Organisation Design. Then work through the rest. Each topic is written for information technology and software, not in general.

Performance Management

Goals, reviews and ratings that hold up in a conversation.

Retention and Attrition Management

Who leaves, when, why — and which of them mattered.

Organisation Design

Structure, reporting lines and who is allowed to decide what.

HR Strategy and Workforce Planning

How many people, in which roles, by when — and what they will cost.

Recruitment and Talent Acquisition

Requisition to joining, the same way every time.

Onboarding and Probation

A first day that is the same for the fifth joiner as the first.

Employee Administration

Records, attendance, leave and the day-to-day HR that must simply work.

Compensation, Payroll and Benefits

Salary structure, payroll inputs and a month that closes on time.

Learning and Development

Training planned from need rather than from the calendar.

Career and Succession Management

Named backups for the roles that cannot be vacant.

Employee Engagement and Experience

Communication, recognition and the conditions people actually work in.

Employee Relations

Two routes — one for complaints, one for conduct — kept separate.

Policies, Compliance and Governance

Policies a manager can apply, and obligations with owners and dates.

Health, Safety and Wellbeing

Induction, records, incident reporting and follow-through.

Exit and Alumni Management

Resignation to relieving letter, in a defined order.

HR Technology and Automation

Records that survive people, and approvals that do not queue.

HR Analytics and Management Reporting

A short monthly pack management actually reads.

HR Operations and Governance

An HR function that runs the same way every month.

What a working HR set-up looks like

The pieces a machine learning companies business puts in place, in an order that makes each easier. Open any card for the detail.

Define research and engineering roles

Clear lines for who owns experimentation and who owns production, with a hand-off.

What this covers

Clear lines for who owns experimentation and who owns production, with a hand-off. Compensation benchmarking helps place pay for scarce skills.

Reward honest negative results

Review experiments openly, record what was learned and treat a clear failure as useful.

What this covers

Review experiments openly, record what was learned and treat a clear failure as useful. Use the performance management process.

Give annotators a career step

Training, quality feedback and a route to reviewer or data operations roles, with decent pay and working hours.

What this covers

Training, quality feedback and a route to reviewer or data operations roles, with decent pay and working hours.

Set fair rules for compute

A simple booking and priority system agreed by leaders, so access is not decided by rank.

What this covers

A simple booking and priority system agreed by leaders, so access is not decided by rank.

Create an ethics channel

A named person or group to receive concerns about bias or data use, with a way to record outcomes.

What this covers

A named person or group to receive concerns about bias or data use, with a way to record outcomes. Confirm legal duties with a qualified professional.

Assign model upkeep

A rota or owner for monitoring deployed models, with time set aside in plans.

What this covers

A rota or owner for monitoring deployed models, with time set aside in plans.

Start by checking where you stand

Before changing anything, find out what is true today. These checks take an afternoon and tell you which of the topics above to start with.

Share your requirements
  • Ask the team to name one failed experiment from this quarter and what it taught them.
  • Look at how annotators are paid, trained and given feedback.
  • Check how compute time is allocated and who decides.
  • List who owns each deployed model in production.
  • Ask whether staff know where to raise an ethics concern.
  • Find out what the strongest scientists were offered elsewhere recently.
  • Confirm with a qualified professional the employment and data-protection duties that apply to your firm.

Confirm with a qualified professional. What applies to you depends on your business, your state and your arrangements, and it changes. This page describes practice. It does not state a legal position.

How GullyHR helps

The services below fit this kind of business best. Each starts by recording where you stand, and every later report compares against that. We do not promise outcomes.

Start with a free conversation, or see the paid HR Diagnostic.

Our HR transformation approach

  1. 1

    Assess

    Understand the business, its workforce, current processes and HR challenges.

  2. 2

    Identify gaps

    Find the missing, weak, inconsistent or person-dependent processes.

  3. 3

    Design

    Create the structure, processes, policies, SOPs, dashboards and systems needed.

  4. 4

    Implement

    Help HR and department managers actually adopt the new system.

  5. 5

    Automate

    Digitise repetitive and approval-based workflows where it is worth doing.

  6. 6

    Measure

    Track hiring, attendance, employee cost, performance, training and attrition.

  7. 7

    Improve

    Strengthen the people-management system continuously, not once.

Blogs worth reading first

From the GullyHR blog: one on HR in information technology and software, and one on each of the topics this page points to.

More on the GullyHR blog.

Questions owners ask

Look at method quality, learning from experiments, collaboration and impact, not just model scores. Uncertain work needs goals that allow for honest failure.

More on HR for information technology and software

All 61 business types in information technology and software · Find another business type

AI product startupsData Processing CompaniesCybersecurity firms3D printing companiesCloud computing companiesRobotics companiesPerformance Management in information technology and softwareRetention and Attrition Management in information technology and softwareOrganisation Design in information technology and software

See all HR topics for information technology and software, or another industry.

Your next chapter

Let’s make HR work better.

Tell us what is slowing your team down. We’ll help you find a practical way forward, starting with a conversation.

  • A free first conversation with an HR practitioner
  • A clear scope and fee before any paid work
  • A practical next step for your business
Have a detailed brief? Share your requirements Or chat on WhatsApp

Let’s start with your business

Share your contact details and the HR challenge on your mind. We’ll help you find the right starting point.

10-digit Indian mobile, without +91.

Your details stay private. Privacy policy