Research Scientist (Engineering)

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source

Seniority

Mid From the posting source

Work mode

Remote From the posting source

In-office days

0 days From the posting source

Schedule

Shift type

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Weekend work

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Company

Equity

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Application

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Research Scientist (Engineering) Remote fal is the generative media ecosystem powering the next generation of AI products. We build the infrastructure, tools, and model access that teams need to move from idea to production, and do it at scale without compromise. For developers and enterprises, fal is the foundation that makes generative media not just possible, but practical: a unified platform where high-performance inference, orchestration, and observability come together to unlock new categories of AI-native products. As generative media reshapes industries across a market projected to grow by hundreds of billions over the next decade, fal is becoming the ecosystem that ambitious teams build on. About this role: You are an ML Researcher who has a broad view of the generative media space and an update-to-date awareness of new methods in the space. You can spot products and features that are missing in the current market and work backwards to develop new methods to solve customers problems. Sometimes your work will require entirely novel training or architecture developments. While other times it will require fine-tuning pre-existing models with novel datasets. You are able to consider the expected return on investment of different approaches, and more excited about using research to develop novel products, then research for research's sake. Tech: - You will have access to our massive GPU cluster for training and inference - Some core technologies we use include Python, torch, diffusers, and the fal Python SDK - You'll work alongside a team dedicated to quickly iterating on and

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