Senior Applied Research Engineer

Senior Applied Researcher AI (Computer Vision / Multimodal)

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Spyne is transforming automotive retail with Generative AI. What began with a simple ideahelp dealers sell cars online fasterhas evolved into the world's first AI-powered automotive retail ecosystem, reshaping how 350,000+ dealers market and sell vehicles globally.

$16M

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Research Lead / Senior Applied Researcher (AI/ML/CV)

build the thing nobody has built before

What You Will Work On

You will design foundational models and agent frameworks across:

Generative Vision

• Premium automotive image creation (Diffusion, 3D, NeRFs, SDFs, Gaussian Splatting)

Voice Intelligence

• Real-time voice agents (ASR, TTS, latency-optimized dialogue models)

Text Intelligence

• Reasoning agents for dealer workflows (lead handling, follow-ups, service flows)

Multimodal & Cross-Modal Learning

• Vision ? Text ? Audio alignment
• Embeddings, routing models, and representational unification

Self-Improving Systems

• RLHF pipelines
• Data-engineered tuning loops
• Autonomous feedback-driven model refinement

You will be the architect who prototypes, validates, breaks, rebuilds, and productionizes new ideas before they scale across the company.

Your Responsibilities

• Design and train novel CV, LLM, and multimodal architectures optimized for dealership workflows.
• Build generative pipelines that convert raw images, video, or speech into structured, high-value outputs.
• Execute applied research in diffusion models, retrieval-augmented LLMs, 3D reconstruction, and voice models.
• Produce clean, reproducible research code with rigorous experimentation.
• Collaborate with infrastructure teams to deploy models at scale with low latency and high uptime.
• Work closely with the AI Research Manager and Product teams to define future research directions.

Job Requirements

• PhD in CS/Engineering from a top university OR Master's with strong CV/GANs experience.
• Publications in peer-reviewed conferences (NeurIPS, CVPR, ICML, ICLR, ICCV, ACL) preferred.
• 25 years experience in Computer Vision, Deep Learning, Generative Modeling, or LLM research.
• Deep understanding of diffusion, transformers, 3D vision, and multimodal architectures.
• Ability to train both large and small language models, and debug complex training behaviours.
• Strong grounding in optimization, probabilistic modeling, and representation learning.
• Experience deploying research models to production is a strong advantage.
• Demonstrated ability to independently explore, prototype, and ship results.

Preferred Background

Experience with one or more of the following:

• Neural Radiance Fields (NeRFs)
• Gaussian Splatting
• Differentiable Rendering
• Diffusion Models
• 3D Diffusion / Generative Models
• Signed Distance Fields (SDFs)
• Prompt Engineering