Research Scientist, Real-Time Interactivity / Inference San Francisco · Research · Full Time →

Reactor; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Generous health, dental, and vision coverage

Visa sponsorship and relocation support

Competitive salary

Job summary

Reactor is seeking a Research Scientist in San Francisco to lead efforts in real-time interactive generation. The role involves working closely with various engineering teams to address challenges in model serving and will require publishing results at top conferences.

The ideal candidate will hold a PhD in relevant fields with a strong research track record, and proficiency in PyTorch and/or JAX. This position offers competitive compensation, health benefits, and support for relocation.

Qualifications

• A track record of research in real-time or streaming video generation.
• Fluent in PyTorch and/or JAX and comfortable with large-scale infrastructure.
• Ability to reason about memory bandwidth.

Responsibilities

• Lead a research agenda on real-time interactive generation.
• Partner with engineers to address bottlenecks in serving models.
• Publish and present research at top conferences.

Skills

Research in real-time video generation

Proficiency in PyTorch

Understanding of latency and memory issues

Experience with ML inference,

Education

PhD in ML, computer vision, graphics, robotics, or equivalent

Tools

CUDA

JAX

Job description

Research Scientist, Real-Time Interactivity / Inference

San Francisco · Research · Full Time

Real-time interactivity can come from inference-time methods applied to an existing model, from architectures designed around latency from the start, or from the interplay between the two. All three matter to us. Our customers bring diverse architectures onto our platform, and we aim to both get more out of what they've already trained and shape how the next generation of models is designed. We're hiring a Research Scientist to lead that work. You'll invent the methods that let people, agents, and robots drive video and world models frame-by-frame. Your work will shape what Reactor's customers ship, and where our platform goes next in real-time interactive world models.

What You'll Do

• Lead a research agenda on real-time interactive generation — inference-time methods, architectural design, and where you think the field should go next
• Partner closely with our inference engineers to understand where the real bottlenecks in serving real-time interactive world models live, and let those challenges shape your research
• Work with model partner teams to build a first-hand view of the broader interactive world model landscape — the architectures, failure modes, and open problems the field is running into
• Publish and present your work at top venues (NeurIPS, ICLR, ICML, CVPR, ICCV, SIGGRAPH, and equivalents)

What We're Looking For

• PhD in ML, computer vision, graphics, robotics, or a related field, or equivalent practical experience
• A track record of research in one or more of: real-time or streaming video generation, autoregressive / causal video diffusion, diffusion distillation, efficient attention or state‑space models for generation, or interactive controllable generation
• You think of latency, memory, and model quality as one problem rather than three
• Fluent in PyTorch and/or JAX and comfortable with large-scale training and inference infrastructure
• Real systems intuition — can read a profiler, reason about memory bandwidth, and have a productive conversation with the kernel engineer next to you
• Value quality over quantity in publishing; treat widely adopted open-source work as a mark of real impact

Strong Candidates May Also Have Experience With

• Real-time interactive generation systems (StreamDiffusion‑style, CausVid / Self Forcing‑style, interactive world model demos, and similar)
• Action conditioning, camera control, or other structured forms of user input
• Writing or modifying CUDA / Triton / custom attention kernels

Representative Projects

• Identifying a recurring failure mode across the world models running on our stack and formulating a robust, generalizable solution
• Proposing a new architecture, training regime, or distillation approach for real-time interactive generation that others build on
• Building a benchmark for long‑horizon coherence under real-time interactive constraints
• A seat on Reactor's research team, with room to influence direction and lead your own research agenda
• A unique vista across the field — real workloads from multiple state‑of‑the‑art model families running on one stack, with direct access to the teams building them
• Close collaboration with a world‑class ML inference engineering team — your research ships on the stack every Reactor customer runs on, reaching people, agents, and robots at scale
• Sufficient compute to train and serve models at scale
• Dedicated support for publishing at top conferences
• Competitive SF salary and meaningful early equity
• Visa sponsorship and relocation support
• Generous health, dental, and vision coverage