Research Engineer - Machine Learning

About the role

Research Engineer - Machine Learning and Systems

Location: New York City Office HQ

Employment Type: Full time

Department: Research

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