A.I. Training Remote Job at Turing

If you’re a machine learning enthusiast with a passion for video data and computer vision, Turing’s latest remote opening for an Applied Research Engineer – Video Data & ML might just be your dream role.

This remote opportunity offers a chance to work with cutting-edge AI technologies, collaborate with top-tier talent, and contribute to real-world systems that make a difference.

Let’s break down everything you need to know about this role—from the company background to job responsibilities, qualifications, and why this position stands out in today’s competitive tech landscape.


About Turing: A Fast-Rising AI Powerhouse

Turing is based in Palo Alto, California, and is quickly becoming one of the world’s most innovative AI companies. Their mission is twofold:

  • Advancing AI research: They work with leading AI labs to push the boundaries of what machines can do—thinking, reasoning, coding, and understanding complex data.
  • Building real-world AI systems: Turing helps Fortune 500 companies and government institutions solve critical problems using AI.

Turing has earned several prestigious awards, including:

  • Forbes’s “One of America’s Best Startup Employers”
  • #1 on The Information’s “Most Promising B2B Companies”
  • Fast Company’s “World’s Most Innovative Companies”

Their leadership team includes experts from Meta, Google, Microsoft, Apple, Amazon, and top universities like Stanford and MIT. Clearly, this is a company that values excellence and innovation.


Role Overview: What You’ll Be Doing

As an Applied Research Engineer focused on video data and machine learning, your main goal will be to improve the quality of video datasets that power advanced AI models.

This role is ideal for someone with 3–5 years of experience in machine learning or computer vision who wants to grow further under the guidance of senior engineers.

Here’s what your day-to-day might look like:

ML-Aligned Data Development

You’ll help design clear guidelines for video annotation tasks such as:

  • Classifying frames and segments
  • Recognizing gestures and actions over time
  • Tracking multiple objects across scenes
  • Labeling interactions between humans and objects

Your work will directly support tasks like action classification, event detection, and object tracking—essential components of modern AI systems.

Benchmark-Driven Data Optimization

You’ll analyze how well models perform on public benchmarks like MVBench, LongVideoBench, and AVA-Bench. If there are gaps in labeling that affect performance, you’ll recommend updates to improve accuracy.

Model Collaboration & Fine-Tuning

Working alongside senior engineers, you’ll:

  • Fine-tune small models like vision transformers or temporal CNNs
  • Run experiments to evaluate annotation quality and model impact

This hands-on experience is perfect for sharpening your technical skills.

QA and Labeling Process Support

You’ll collaborate with QA leads to:

  • Build gold-standard datasets
  • Create error-checking protocols
  • Improve consistency in labeling

This ensures that the data used to train models is reliable and high-quality.

Cross-Functional Communication

You’ll act as a bridge between machine learning engineers, annotators, and QA reviewers. Clear documentation and communication will be key to keeping everyone aligned.


Required Skills and Qualifications

To succeed in this role, you’ll need:

  • 3–5 years of experience in computer vision or applied ML
  • Knowledge of video modeling techniques like tracking and segmentation
  • Hands-on experience with ML tools like PyTorch, TensorFlow, or Hugging Face
  • Familiarity with video labeling tools such as CVAT, VOTT, Labelbox, or SuperAnnotate
  • Understanding of the ML data lifecycle, including synthetic data and human-in-the-loop systems
  • Ability to read research papers and apply insights to real-world problems
  • Strong communication skills for working across teams

If you meet most of these qualifications, you’re already a strong candidate.


What Success Looks Like

Turing defines success in this role as:

  • Building scalable annotation pipelines that improve model accuracy
  • Creating well-documented workflows that reduce errors
  • Making hands-on contributions to model fine-tuning
  • Collaborating effectively across teams to speed up development

This means your work will have a direct impact on the performance and reliability of AI systems used by major organizations.


Compensation and Perks

Turing offers a competitive compensation package:

  • Base salary: $170,000 – $200,000
  • Equity: You’ll receive company shares, giving you a stake in Turing’s success

Other benefits include:

  • A collaborative and supportive work culture
  • Flexible working hours
  • Full-time remote work
  • Opportunities to work with top talent from companies like Meta, Google, and LinkedIn

Diversity and Inclusion

Turing is committed to building a diverse and inclusive workplace. They encourage candidates from all backgrounds to apply—even if you don’t meet every single qualification. Studies show that women and people of color often hesitate to apply unless they meet all criteria, but Turing wants to change that.

They do not discriminate based on race, gender, age, disability, or any other protected characteristic. If you’re passionate about the role, they want to hear from you.


Final Thoughts: Is This Role Right for You?

This job is perfect for someone who:

  • Has a solid foundation in machine learning and video data
  • Wants to work remotely with flexible hours
  • Enjoys collaborating with top-tier professionals
  • Is eager to grow through mentorship and hands-on experience
  • Values diversity, inclusion, and innovation

Whether you’re looking to deepen your technical skills or contribute to impactful AI systems, this role offers a unique opportunity to do both.


How to Apply

Ready to take the next step? You can apply directly through Turing’s website:

If you’re in the European Union, make sure to review their GDPR notice before applying.


Bonus Tip for Applicants

Before applying, consider brushing up on:

  • Video annotation tools like CVAT or Labelbox
  • Benchmark datasets used in video ML tasks
  • Fine-tuning techniques for small models
  • Recent research papers on video understanding

This will help you stand out and show that you’re serious about the role.

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