AI Engineer - R&D to Production Multi-Modal Video, Text, Audio

Experience: 4–5+ Years Employment Type: Full-Time Hiring Partner: Flatgigs Client: Confidential AI Research & Innovation Lab

About the Opportunity

Flatgigs is hiring AI Engineers for a confidential R&D-focused AI lab working across video, text, and audio problem spaces .

This is not a typical “apply a model and ship” role. This is a research-driven environment where problems are not clearly defined, and solutions are not obvious.

You will spend time:

• Exploring
• Experimenting
• Failing
• Iterating
• And eventually solving problems that don’t have playbooks

If you need structured tasks and predictable outputs — this role will frustrate you. If you enjoy going deep into problems and figuring things out from first principles — this is where you’ll thrive.

What You’ll Work On

• Solve complex AI problems across:
• Video understanding and processing
• Text-based AI / NLP systems
• Audio processing (nice to have, not mandatory)
• Design, experiment, and iterate on AI models and approaches
• Work on problems that may not have clear starting points or predefined solutions
• Explore multiple approaches before arriving at optimal solutions
• Contribute to research-driven experimentation and innovation

What We’re Looking For

Core Requirement

• 4–5+ years of experience in AI / Machine Learning

This level of experience matters because it typically brings exposure to multiple AI domains , not just one.

Multi-Modal Exposure (Important)

• Experience across at least two of the following areas :
• Video
• Text (NLP / LLMs)
• Audio (bonus)

👉 Candidates limited to only one vertical may struggle in this environment.

R&D Experience (Critical)

• Prior experience working in research or experimentation-heavy environments
• Exposure to:
• Prototyping
• Testing hypotheses
• Iterative experimentation
• Experience contributing to:
• Research initiatives
• Internal innovation projects
• Publications (nice to have, not mandatory)

Problem-Solving Depth

• Ability to go beyond “making things work” and instead:
• Understand why something works or fails
• Debug deeply at the model or system level
• Explore multiple approaches before finalizing a solution

Ideal Candidate Profile

• Curious by nature — asks “why” more than “what”
• Comfortable working in uncertain, ambiguous problem spaces
• Enjoys experimentation and is not discouraged by failure
• Thinks in terms of systems, patterns, and root causes
• Has a tinkerer mindset — explores beyond surface-level fixes