Research Scientist- SLAM & VIO

Kindredventures; New York (NY); On-site; USD 150,000 - 210,000; Full time

Job summary

Mecka AI in New York seeks a Research Scientist to architect and train foundation models from scratch for spatial-temporal perception in embodied AI. You will lead development of 3D geometry extraction networks and egocentric optical flow models using proprietary ground-truth data.

You will scale training across multi-GPU clusters, explore novel losses and CUDA-accelerated ops, and advance dense reconstruction techniques (NeRFs) for photorealistic environments, while adapting quickly to evolving

Qualifications

• Deep knowledge of modern deep learning, 3D vision, and temporal/video modelling.
• Proven track record training large-scale vision models from scratch.
• Strong understanding of feed-forward 3D networks and dense motion estimation.
• Mastery of PyTorch and scalable DL frameworks.
• Experience handling multi-terabyte image/video datasets.

Responsibilities

• Architect and train proprietary spatial models from scratch.
• Scale multi-GPU distributed training for multi-view datasets.
• Develop novel losses and attention mechanisms for 3D data distributions.
• Build egocentric optical flow models and temporal dynamics for dynamic scenes.
• Prototype and deploy models for tracking, segmentation, and fusion in robotics contexts.
• Integrate models into dense reconstruction pipelines (e.g., NeRFs).

Skills

Deep learning

3D vision

Temporal modeling

PyTorch

Large datasets

Adaptability

Tools

CUDA

Ray marching

Correlation volumes

Job description

About Mecka AI

Mecka AI is building the data infrastructure layer for robotics and embodied AI.
We design and operate global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems.
We work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware.

The Role

While our existing perception division handles classical state estimation and VIO, this role is dedicated to the next generation of spatial and temporal intelligence. We are hiring a Research Scientist to architect and train proprietary foundation models from scratch.

Your core mandate is twofold: building our in-house equivalents to cutting-edge 3D reconstruction architectures, and developing highly robust optical flow models tailored for the chaotic domain of egocentric vision. Beyond these core pillars, you will serve as a lead problem-solver for emergent perception challenges as our hardware and downstream robotics needs evolve.

To achieve this, we can provide a massive, continuous stream of high-quality, proprietary ground-truth data captured by our infrastructure. You will use this data advantage to train networks that surpass current public baselines, owning the complete spatial-temporal perception loop for our data engine.

What You’ll Work On

Architecting Proprietary Spatial Models

• Zero-to-One Model Development: Design, implement, and train state-of-the-art feed-forward network and per-scene differential optimization architectures for 3D geometry extraction.

• Large-Scale Distributed Training: Scale multi-view ML architectures across multi-GPU clusters to handle massive, multi-modal spatial datasets.

• Loss & Architecture Innovation: Push the boundaries of current paradigms by developing novel loss functions and attention mechanisms tailored to our specific data distributions.

Egocentric Optical Flow & Temporal Dynamics

• Egocentric Motion Modeling: Build and train custom optical flow architectures capable of handling the extreme motion blur, rapid rotations, and sudden occlusions inherent in head-mounted or robot-mounted cameras.

• Dynamic Scene Understanding: Use your flow models to segment dynamic actors, track objects through complex manipulations, and provide motion regularization for downstream action-conditioned world models.

Emergent Perception R&D

• Rapid Prototyping: Tackle novel, unmapped AI challenges as they arise. You will rapidly prototype and deploy new models for tasks spanning tracking, segmentation, multi-modal sensor fusion, and beyond.

• Agile Problem Solving: Pivot to resolve sudden algorithmic bottlenecks in the data engine, adapting the latest research to unblock new product capabilities or hardware integrations.

Next-Level Dense Reconstruction

• Neural Rendering Integration: Connect the outputs of your foundational models into highly optimized, large-scale dense reconstruction pipelines (3D Gaussian Splatting, NeRFs) to generate photorealistic environments.

What You Bring

Required Background

• Deep expertise in Deep Learning, 3D Computer Vision, and Temporal/Video Modeling.

• Proven experience training large-scale vision models from scratch, not just running inference or fine-tuning.

• Strong theoretical and practical understanding of modern feed-forward 3D networks and dense motion estimation.

• Mastery of PyTorch and deep learning scaling frameworks.

• Experience handling and curating massive, multi-terabyte image and video datasets for training.

• Comfortable operating in a fast-paced environment where priorities can shift rapidly to capitalize on new research or hardware capabilities.

Strong Signals:

• First-author publications in top-tier venues (CVPR, ICCV, ECCV, NeurIPS) focusing on 3D deep learning, optical flow, video generation, or spatial transformers.

• Specific experience working with egocentric video datasets (e.g., Ego4D, Ego-Exo4D) and solving the unique optimization challenges they present.

• Experience writing custom CUDA kernels to accelerate 3D operations, ray marching, or correlation volume computations.

Why This Role?

• The Data Advantage: You will have access to a scale and quality of proprietary spatial and temporal ground truth that most academic researchers only dream of.

• Pure R&D & Model Ownership: You are not maintaining legacy systems; you are given a blank slate and the compute resources to build the state-of-the-art.

• High Impact: The spatial priors and motion models you architect will directly define how the next generation of embodied AI agents perceive and move through the physical world.

Warning: Research Scientist positions require hyper-specific expertise. Please limit your applications to one research role. Applying to multiple Research Scientist positions suggests a lack of focus and may result in the rejection of all submissions. You may, however, apply to other non-research roles alongside your research application.

A Note on Applying

Studies show women and candidates from underrepresented groups often only apply when they meet 100% of the listed qualifications, while others apply after meeting 60%. If you don't check every box above but believe you can do the job, we encourage you to apply— we're looking for capability and trajectory, not a perfect checklist match.
Inclusive Hiring at Mecka

We are committed to creating an inclusive and supportive candidate experience. Should you require any accommodation whatsoever during the interview process, please inform us without any hesitation. Mecka is dedicated to ensuring equal treatment and opportunity in all phases of recruitment, selection, and employment, in compliance with employment law. We do not discriminate based on gender, race, religion, national origin, ethnicity, disability, gender identity/expression, sexual orientation, veteran or military status, or any other protected category. Mecka is proud to be an equal opportunity employer, fostering a culture of inclusivity and maintaining a work environment that is free from discrimination, harassment, and retaliation.

Use of Artificial Intelligence in Recruitment

Mecka uses artificial intelligence (AI) responsibly to support administrative and efficiency-focused aspects of our recruitment process. This includes activities such as drafting job descriptions, generating interview questions, note-taking and recordings, and supporting sourcing and scheduling workflows. All candidate evaluations, interviews, and hiring decisions are made by members of the Mecka team. While AI tools may assist with screening and assessment, they do not replace human judgment in selection decisions. Our use of AI is intended to streamline routine tasks, improve consistency, and enhance the overall candidate experience. We are committed to upholding principles of fairness, transparency, and accountability in all hiring activities. Mecka regularly reviews its recruitment practices to mitigate bias and to ensure alignment with applicable laws and evolving best practices.