Research Engineer - Machine Learning

About True3D Labs

At True3D we are building the next medium after film. Our team works at the edge of graphics, compression, and AI to turn moving pictures into experiences you can stand inside. This is not incremental work. It is a reinvention of how video is captured, streamed, and remembered.

We are a small focused crew with roots at places like Meta and TikTok. Our compass points forward. We build with curiosity, intensity, and craft, and we share our experiments in public at splats.com. If you join us, you will be expected to do the best work of your career and to shape both the research frontier and the systems that bring it to life.

You will work with peers who set a high technical bar and who care about storytelling as much as they care about code. You will ship quickly, push past what is thought possible, and see your work ripple across research, media, and culture.

If you want to help create the medium that will replace flat video and you thrive when the challenge is steep and the impact is lasting, you will feel at home here.

About the Role

Overview

We are hiring a principal level Research Engineer with deep strength in machine learning or 3D graphics, software engineering, and systems design. You will bridge frontier research with production systems and ship advanced models used in real products. The work spans exploration, rapid prototyping, rigorous experimentation, and dependable production deployment. Expect to push the limits of spatial intelligence and controllable graphics while keeping systems robust, scalable, and cost efficient.

Role

You will partner with research, engineering, and product to design, build, and operate large models and high performance systems. You will set technical standards, mentor others, and raise the bar for research quality, code quality, and reproducibility.

Key Responsibilities

• Research, design, and implement models and systems across vision, generative modeling, simulation, rendering, and 3D perception
• Build data, training, evaluation, and deployment pipelines with strong observability and reproducibility
• Translate research insights into reliable production services that meet product and latency requirements
• Contribute hands on across prototyping, optimization, integration, and scaling
• Survey new methods and run grounded evaluations to identify what to adopt and when
• Share expertise through design reviews, mentoring, and documentation

Minimum Qualifications

• PhD in Computer Science, Machine Learning, Computer Graphics, Computer Vision, or related field, or equivalent research track record
• Seven or more years of experience in applied ML or research engineering including significant time in fast paced or startup settings
• Strong publication record in top venues such as NeurIPS, ICLR, ICML, CVPR, ECCV, ICCV, SIGGRAPH, or TOG with multiple first author papers or equivalent impactful artifacts
• Proven experience training and serving large models at scale including multi GPU or multi node training, distributed data loading, mixed precision, and memory optimization
• Fluency in Python and C++ and experience writing efficient CUDA or Triton kernels
• Expertise with PyTorch or JAX and modern tooling for experiment tracking, evaluation, and deployment
• Demonstrated ability to take ideas from paper to production with measurable impact on users or business outcomes
• Strong systems skills including profiling, performance tuning, reliability engineering, and cost awareness
• Excellent communication with the ability to work across research and product teams

Preferred Qualifications

• Contributions that are widely used in the community such as open source libraries, datasets, or benchmarks with visible adoption
• Experience in neural rendering, differentiable rendering, 3D reconstruction, volumetric video, SLAM, geometric deep learning, or simulation
• Experience operating large training jobs on Kubernetes, Slurm, or Ray across public cloud environments
• Experience with evaluation and safety for generative or interactive models including red teaming and guardrail design
• Track record of mentoring teams and setting research and engineering best practices
• Patents or awards that recognize technical contributions

Nice to have

• Shipped interactive graphics or 3D systems with strict real time constraints
• Experience building custom compilers or graph level optimizations such as CUDA graphs, XLA, or graph capture
• Prior leadership in cross functional initiatives spanning data, infra, and product

How to Apply

Please include a CV, links to publications, code, and a brief summary of two projects that best represent your impact. Include details on model scale, data scale, latency or throughput targets, and the concrete results you achieved.

On site in New York City required. Relocation support available.