Level

Twitch

Applied Scientist

Twitch is hiring an Applied Scientist in San Francisco, United States. Level rates it ; you can apply on Level.

AI in this role

pytorchtensorflow
ai-evaluationcomputer-vision

About Us

Twitch is the world’s biggest live streaming service, with global communities built around gaming, entertainment, music, sports, cooking, and more. It is where thousands of communities come together for whatever, every day.

We’re about community, inside and out. You’ll find coworkers who are eager to team up, collaborate, and smash (or elegantly solve) problems together. We’re on a quest to empower live communities, so if this sounds good to you, see what we’re up to on LinkedIn and X,  and discover the projects we’re solving on our Blog. Be sure to explore our Interviewing Guide to learn how to ace our interview process.

About the Role

We are looking for an Applied Scientist to solve challenging and open-ended problems in the domain of Creators and content contextual signals. As an Applied Scientist on Twitch's GTM Science team, you will use applied machine learning to tackle ambiguous business problems across problem areas such as Ads, Brand Safety, Creator Sponsorship and Content Understanding. You will lead high profile projects where you will use a wide toolbox of ML tools to handle multiple types of data, including user behavior, metadata, and user generated content such as text and video. You will collaborate with a team of passionate scientists, engineers, and product stakeholders to develop these models, prototype and put them into production, where they can help Twitch's creators and viewers succeed and build communities.

You will report to our Senior Data Science Manager for GTM. This position is located in San Francisco, CA.

You Will:

  • Build machine learning products to enrich Twitch’s content understanding and help creators and viewers build and discover their communities. 
  • Propose, design, and oversee execution for high-impact projects that work backwards from customer problems to develop the right solution for the job. Know when to use a classical ML model versus a state-of-the-art one.
  • Collaborate with cross-functional engineering and product teams to prototype and deploy your models into flexible data pipelines and ML-based services.
  • Bring innovation and help drive science culture on the team by staying up to date and experimenting with new techniques in LLM, computer vision, inference and model evaluation. 

You Have:

  • A degree in a quantitative field (Computer Science, Mathematics, Statistics, Physics, Engineering, or similar) with:
    • 5+ years of hands-on industry experience with applied ML (Bachelor's), OR
    • 3+ years of applied ML experience (Master's), OR
    • 1+ year of applied ML experience (PhD)
  • Experience with LLM model evaluations.
  • Experience owning projects that involve numerous scientists and engineers.
  • Experience working with both technical and non-technical partners and stakeholders.
  • Expertise with: Python and SQL, modern open source ML libraries (Pytorch, Tensorflow, etc.). Developing software in AWS or similar cloud-computing services.

Bonus Points

  • PhD with a specialization in Machine Learning.
  • Experience with computer vision, LLM inference, content understanding. 
  • 1+ years of experience working in GTM / Content / Creator orgs (not necessarily as a scientist).
  • Familiarity with Twitch, its business, and its community.

Perks

  • Medical, Dental, Vision & Disability Insurance
  • 401(k)
  • Maternity & Parental Leave
  • Flexible PTO
  • Amazon Employee Discount

 

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. 

Job ID: TW9302

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

US, CA, San Francisco$171,600—$222,200 USD

Twitch is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Twitch values your privacy. Please consult our Candidate Privacy Notice, for information about how we collect, use, and disclose personal information of our candidates.

How we rate this

Applied Scientist at Twitch rates 92 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

AI EvaluationComputer visionPyTorchTensorFlow

Questions you could be asked

  1. How do you decide that one model's output is better than another's for a given task?
  2. Walk me through a computer vision problem you solved, from raw data to a deployed model.
  3. What are the limits of PyTorch that you've run into, and how did you work around them?
  4. What's a project where you used TensorFlow hands-on?
  5. How would you decide a model or AI system is ready to ship?

Adapt your resume

  • List these exact terms on your resume: AI Evaluation, Computer vision, PyTorch, and TensorFlow. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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