design Jobs
Designer Jobs19 Positions Available
Find 19 Designer jobs hiring now. Browse 4 featured positions from top companies. Filter by location, salary, and experience level.
19+
Active Jobs
13+
Companies
2+
New This Week
Featured Designer Positions
3 FeaturedDesigner Jobs by Location
Find Designer positions in your preferred city
🇮🇳 India
0 citiesComing soon
🇦🇪 Dubai / UAE
0 citiesComing soon
🇺🇸 United States
0 citiesComing soon
All Designer Openings
Designer Career Resources
Prepare for your Designer job search with our comprehensive guides
Preparation
Interview Questions
Practice with common Designer interview questions and expert answers
Practice now
Resume
Resume Keywords
Optimize your resume with ATS-friendly keywords for Designer roles
Optimize now
Compensation
Salary Guide
Research Designer salary ranges and negotiation tips
View salaries
Application
Cover Letter Examples
Stand out with professional Designer cover letter templates
View templates
Related design Roles
Explore similar positions in design that match your skills and interests.
Join us as we build the future of skills-based hiring
One platform. Two broken systems solved. Built for better outcomes.
Get ready to stop wasting time on hiring that doesn’t work.
Picture this:
Next Monday, you post a job.
By Wednesday, you have ranked candidates who’ve proven they can do the work.
By Friday, you’re making an offer you trust — because data backs your decision.
That’s Hirenest.
No credit card • No setup friction • Just better hiring
FAQ
Questions? We've Got You Covered
Real answers to the questions founders actually ask about automated content and Google penalties.
A Machine Learning Engineer is a software engineer who specializes in building, deploying, and maintaining machine learning models in production environments. They bridge the gap between data science and software engineering, ensuring that ML models are scalable, reliable, and maintainable.
Key skills include strong programming fundamentals (Python, C++), expertise in machine learning frameworks (TensorFlow, PyTorch, scikit-learn), understanding of data structures and algorithms, experience with cloud platforms (AWS, GCP, Azure), knowledge of MLOps practices, and strong problem-solving abilities.
Salaries vary widely based on experience, location, and company. In the US, entry-level ML engineers can expect $100K-$140K, mid-level engineers $140K-$180K, and senior/lead engineers $180K-$250K+. Top companies in major tech hubs can offer significantly higher compensation packages.
Data Scientists focus on analyzing data, extracting insights, and building predictive models. Machine Learning Engineers focus on deploying these models into production systems, ensuring scalability, and maintaining them over time. Data Scientists ask 'what can we learn from this data?', while ML Engineers ask 'how can we productionize this model?'
MLOps (Machine Learning Operations) is a set of practices that combines machine learning, DevOps, and data engineering to deploy and maintain ML models in production reliably and efficiently. It focuses on automation, version control, continuous integration/continuous delivery (CI/CD), monitoring, and governance of ML systems.
Career paths include Senior ML Engineer, Staff ML Engineer, ML Team Lead, MLOps Engineer, AI Engineer, Research Engineer, and eventually roles like Director of Machine Learning or VP of Engineering. Many also transition into product management or specialized AI roles.
Recommended resources include Andrew Ng's Machine Learning Specialization on Coursera, fast.ai's Practical Deep Learning for Coders, DeepLearning.AI's MLOps Specialization, books like 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow', and platforms like Kaggle for hands-on practice.
The job outlook is excellent. The demand for skilled ML engineers is rapidly growing across all industries as companies increasingly rely on AI and machine learning to drive business value. Many reports project double-digit annual growth in ML engineering roles over the next decade.
Not necessarily. While many senior research roles prefer PhDs, most ML engineering positions value practical experience and proven skills over advanced degrees. A strong portfolio, relevant projects, and demonstrated expertise in ML frameworks are often more important than a PhD.
Day-to-day tasks include developing and training ML models, implementing ML algorithms, deploying models to production, monitoring model performance, optimizing existing models, collaborating with data scientists and software engineers, and maintaining ML infrastructure.
Connect with opportunities and talent through validated skills and AI-powered matching.