How to hire TensorFlow developers

The right TensorFlow developer can help your business make full use of artificial intelligence (AI) and machine learning (ML).

But TensorFlow development, like machine learning in general, requires more than technical skills. The best developers demonstrate intuition and innovation as they create models and wrangle bugs and unexpected behavior. 

Companies must consider several important factors before hiring a new TensorFlow developer, and finding developers will require some creative thinking. 

In this article, we explore solutions to these problems and more, including all the hard and soft skills to look for and how best to assess TensorFlow candidates

What you need to know before hiring a TensorFlow developer

TensorFlow is an open-source machine learning framework developed by Google. It's used to build and train machine learning models, especially deep learning models, which are known for their ability to handle complex tasks like image recognition and natural language processing.

Before hiring a developer, carefully consider the following points:

When do you actually need a TensorFlow developer?

When your project moves beyond basic machine learning to involve complex tasks like deep learning, neural networks, or advanced data processing, it's time to hire a dedicated TensorFlow developer. 

This need typically arises when dealing with large datasets, requiring sophisticated model training and optimization, or when your AI initiatives are scaling up and demand specialized expertise for effective implementation.

What seniority, skills, and experience are needed for your project?

For projects at an early stage or those requiring innovative ML model development, a senior developer with extensive TensorFlow experience and a solid machine learning theory background is essential. 

Conversely, for more implementation-focused projects, a mid-level developer proficient in applying pre-built TensorFlow models may suffice. Consider the project's scope and complexity to decide the level of expertise needed.

TensorFlow is a rapidly evolving platform

TensorFlow’s landscape is continually evolving with new features and updates. This rapid development pace means a potential candidate should not only be proficient with current TensorFlow capabilities but also be adaptable and eager to learn. 

Look for developers who demonstrate a commitment to continuous professional development and show awareness of the latest trends and advancements in TensorFlow technology.

Most developers don’t specialize in just TensorFlow

It's not very common to find developers who specialize solely in TensorFlow. Typically, TensorFlow knowledge is part of a broader skill set that includes proficiency in Python, data science, and other machine learning frameworks. 

Therefore, while you should primarily look for TensorFlow expertise, consider candidates with a well-rounded background in related technologies. This broader expertise often translates to a more versatile approach to problem-solving and innovation in TensorFlow projects.

TensorFlow developers can command high rates (with good reason)

TensorFlow developers possess a unique blend of skills in machine learning, deep learning, and data science, along with proficiency in a specialized, high-demand technology. Understandably, this means they command a high salary. You must be prepared to offer TensorFlow developers appropriate compensation. 

To craft a compelling offer, check out industry surveys and reports, other job posts, professional networks and forums, recruitment agencies, and websites like Salary.com.

Skills and qualifications to look for in a TensorFlow developer:

Of course, development with machine learning requires strong technical skills. But there’s also a need for intuition and creativity. ML models don’t always react as expected, and some are so complex that developers can no longer simply “pop open the hood” and inspect what’s gone wrong.

A good TensorFlow developer will combine these hard and soft skills: 

Hard skills

• Knowledge of the TensorFlow ecosystem: Candidates should demonstrate in-depth knowledge of TensorFlow’s libraries and tools, including understanding TensorFlow 2.x, Keras for building and training models, and TensorFlow.js for deploying models in a web environment.

• Experience with ML algorithms: A solid grasp of machine learning algorithms is crucial, especially those commonly implemented in TensorFlow, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers.

• Data preprocessing and analysis: Developers must demonstrate skill in handling and preprocessing large datasets to make them suitable for use with TensorFlow models. This includes familiarity with TensorFlow Data Services (TFDS) and data augmentation techniques.

• Optimization and deployment: Experience optimizing TensorFlow models for performance and efficiency and deploying them in different environments, including TensorFlow Lite for mobile and TensorFlow Extended (TFX) for production pipelines.

• Solid understanding of deep learning: An in-depth understanding of deep learning concepts like backpropagation, dropout, and batch normalization – how they’re implemented and optimized in TensorFlow.

Soft skills

• Intuitive problem solving: The ability to intuitively understand complex ML models is key. TensorFlow developers often face unpredictable model behavior. An intuitive approach, combined with technical know-how, helps developers identify and resolve issues that aren't immediately apparent.

• Creativity and innovation: ML development, particularly with TensorFlow, frequently ventures into uncharted territories. Creativity in model design and implementation can lead to breakthroughs, especially when standard approaches fall short.

• Analytical thinking: Given the complexity of ML models, a TensorFlow developer must possess strong analytical skills. This involves breaking down complex problems and understanding the interplay between various model components to diagnose issues effectively.

• Patience and perseverance: ML models, especially in TensorFlow, might require numerous iterations and extensive fine-tuning. A developer needs patience and perseverance to experiment with different approaches and achieve optimal results.

• Strong communication skills: Conveying complex TensorFlow concepts to team members who might not have a deep understanding of ML requires clear and effective communication. This skill is crucial for collaborative problem-solving and project advancement.

These soft skills, combined with technical prowess in TensorFlow, make for a well-rounded developer. They ensure that the developer is not just technically adept but also capable of navigating the intricate and often unpredictable nature of machine learning development.

Where to find skilled TensorFlow developers

To find the best talent, you’ll want to post and promote your TensorFlow job description where the most skilled candidates will likely see it. Here are some places you can start:

• Online tech communities and forums. Platforms like GitHub, Stack Overflow, and Reddit (in subreddits like r/MachineLearning or r/DeepLearning) are good places to find active TensorFlow and ML specialists. Engaging in these communities can help identify potential candidates who demonstrate expertise through their contributions and interactions. Pro Tip: Check out “Newest ‘TensorFlow’ Questions” on Stack Overflow.

• The TensorFlow.org User Group. The TFUG includes a forum, groups, and local groups. You can even hunt around the Bug Reports and see which developers are most active in finding bugs and contributing to TensorFlow’s development. 

• Professional networking sites. LinkedIn is a valuable resource for finding professionals of all kinds, including developers. You can use targeted search filters to find individuals with TensorFlow and ML expertise. Additionally, LinkedIn groups dedicated to AI, ML, and TensorFlow can be a source of potential candidates. Pro Tip: Check out these LinkedIn groups that mention TensorFlow as a keyword.

• Specialized job boards: Job boards focusing on tech and AI roles often attract specialized talent in machine learning and TensorFlow. Here are some popular ones you can try posting to:

  • Deep Learning Jobs

  • The Machine Learning Conference (MLconf) job board

  • Scalable Path

• Colleges, universities, and other academic institutions. Universities known for strong AI and ML programs can be a source of emerging talent. Many universities even have their own job boards that you can post to. Take, for example, the MIT Students’ Jobs Board. You can specify post-graduate students if an undergraduate degree is a requirement for your role. This is also a great place to find part-time TensorFlow developers if that’s all you need. 

3 creative ways to find TensorFlow developer candidates

In times of talent scarcity like today, you sometimes need to get creative to find the best talent. These three strategies will help you expand your talent pool:

1. Tech meetups and hackathons

You can participate in or host machine learning and TensorFlow-focused tech meetups and hackathons. These events are hotspots for collaboration and problem-solving (essential skills for TensorFlow developers) and offer a unique opportunity to observe potential candidates’ abilities in real time.

2. Open Source contributors

Tap into the open-source community, especially projects aligned with machine learning and TensorFlow. Active contributors often possess the expertise and passion you need. Recognizing their work or collaborating on projects can be a gateway to discovering talented individuals. You can access the TensorFlow community here. 

3. Remote talent platform

Remote work platforms like Toptal and Upwork give you access to a global talent pool. Remote work options not only widen your search but also attract candidates who value flexibility and may contribute diverse experiences to your project.

How to select the best TensorFlow developer candidates

Selecting the best TensorFlow developer requires a nuanced approach. You’ll need to blend traditional assessment methods with strategies specific to machine learning and TensorFlow expertise. 

Here’s a quick guide to help you identify the right candidate:

1. Evaluate technical skills through specialized tests

You can use specific programming skills tests, like TestGorilla’s TestGorilla TensorFlow Test, to objectively assess candidates' proficiency in TensorFlow. This test evaluates their understanding of TensorFlow's libraries, APIs, and machine learning algorithms, ensuring they have the necessary technical foundation.

2. Review past projects and portfolios

Ask candidates to present their previous TensorFlow projects. This allows you to assess their experience implementing machine learning models, handling data sets, and resolving issues in real-world scenarios.

3. Conduct technical interviews with situational questions:

Use scenario-based questions in interviews to evaluate candidates’ problem-solving, critical thinking abilities, and situational judgment. Pose challenges they might encounter while working on TensorFlow projects, such as optimizing model performance or dealing with large, unstructured datasets.

4. Assess their understanding of Machine Learning concepts

Ensure the candidate has a solid grasp of core machine learning concepts, as TensorFlow is deeply rooted in these principles. Understanding concepts like overfitting, underfitting, and model validation is crucial for effective TensorFlow development. 

We recommend using our Machine Learning test, which covers classical regression methods and regularization, tree-based and ensemble methods for regression and classification, classical classification methods and support vector machines, unsupervised learning, outlier detection, and dimensionality reduction. 

5. Evaluate candidates’ ability to keep up with changes to TensorFlow

In interviews, discuss with candidates how they stay updated with the latest TensorFlow developments. Their approach to continuous learning and adaptation is key, given the platform's rapid evolution.

6. Assess collaborative and communication skills

Considering the collaborative nature of TensorFlow projects, it’s important to assess candidates' ability to work in a team and communicate their ideas effectively. Use TestGorilla’s Communications Test and Intermediate Communications Test for this.

7. Test problem-solving skills with practical exercises

In addition to our Problem Solving test, you can offer practical exercises or even small projects related to your specific use case while assessing candidates. Doing so can reveal how candidates approach problem-solving in a hands-on setting and their ability to apply TensorFlow skills effectively.

8. Consider culture add and long-term potential

While technical skills are paramount, it’s essential not to overlook the importance of culture add and alignment with your company's values and long-term goals.

Add the following TestGorilla tests to your assessment for TensorFlow candidates:

• Culture add test

• Motivation test

• Enneagram Personality test

• 16 Personalities test

• DISC Personality test

Selecting the best TensorFlow developer goes beyond assessing technical skills. Use a combination of specialized tests, practical assessments, and interviews focused on machine learning principles and TensorFlow specifics to find the right candidate for your team.

2 common mistakes when hiring a tensorflow developer

Watch out for these two common missteps as you assess candidates:

1. Overemphasizing traditional training or qualifications

Many of the most skilled developers in the world are STARs – Skilled Through Alternative Routes – so try to be open to all types of training routes. 

This is especially important when hiring TensorFlow developers. TensorFlow was created in 2015 and is a relatively niche specialty. No universities offer degrees in it, and most individuals will be self-taught. (There is, however, a TensorFlow Developer Certificate you may ask candidates to highlight if they have it.)

2. Focusing too much on technical skills

When assessing candidates in machine learning and artificial intelligence, it can be tempting to focus on very technical skills – to the exclusion of all others. This is especially true for candidates without ML or AI backgrounds.

However, TensorFlow and machine learning require great intuition, creativity, and innovation. So remember to look for candidates who demonstrate these skills in addition to technical expertise.

Use TestGorilla to identify top TensorFlow talent

Hiring a TensorFlow developer can significantly enhance your AI and machine learning projects, bringing specialized skills in handling complex data and neural networks.

Start by identifying your project's specific TensorFlow requirements and then explore tech communities, open-source platforms, and remote talent pools for candidates. Be sure to write a compelling, precise TensorFlow job description, too.

Use tools like the TestGorilla TensorFlow Test for a precise, unbiased evaluation of candidates’ technical skills, then add tests for culture add, creativity, problem solving, and more. 

By focusing on technical expertise and team fit, you can select the ideal TensorFlow developer to drive your AI initiatives forward.

Join TestGorilla for free or schedule a product demo to improve your hiring process today.