FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Research Scientist – Geo-Distributed Inference
Pluralis Research. Build and own the inference stack end-to-end .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and managing large-scale inference systems, with a focus on serving-engine internals and pipeline-parallel execution. Proficient in handling low-bandwidth, high-latency environments and familiar with reinforcement learning post-training.
Highest-signal resume keywords
Large-Scale Inference System DevelopmentServing-Engine InternalsP2P Networking and NAT TraversalReinforcement Learning Post-TrainingPublications in Distributed Inference
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Algorithm DesignFast Inference TechniquesPipeline ParallelismModel Serving DevelopmentDistributed Systems
Soft Skills
Professional-Level English ProficiencyAbility to Work Across Timezones
Tools & Technologies
Apple SiliconMLX
Industry Keywords
Protocol LearningDecentralized TrainingConsumer HardwareReinforcement LearningPublic Internet
About the role
Key responsibilities & impact- Build and own the inference stack end-to-end
- Set direction for pipeline-parallel execution, placement and routing, transport, serving engine, and failure handling
- Invent, design, validate, and productionize algorithms for fast inference on consumer hardware over the public internet
- Keep the rollout pipeline fast and reliable for reinforcement learning training
- Develop the serving layer for models once trained
- Support inference across Macs and consumer GPUs owned by distributed participants
- Handle nodes joining and leaving mid-run and model weights changing during training
Requirements
What you’ll need- Experience shipping serving-engine internals or building a large-scale inference system
- Hands-on ability to work on serving systems
- Publications in distributed inference or a nearby field, such as LLM serving systems, pipeline parallelism over slow networks, or decentralized training, or equivalent unpublished work that can be discussed
- Experience with low-bandwidth, high-latency systems operating over the public internet
- Belief in Protocol Learning as a viable approach for collective, trustless, and sovereign AI
- Familiarity with RL post-training
- Exposure to Apple silicon or MLX
- Experience with P2P networking and NAT traversal
- Experience at proprietary, open-weight, or open-source AI labs
- Professional-level English proficiency, written and spoken
- Ability to work across timezones
Benefits
Comp & perks- Significant equity ownership for key technical contributors in addition to a high base salary
- Flexible work environment
- Optional full visa sponsorship
- Relocation support to Australia or the US
- Opportunity to work on open, frontier research problems
- Remote-first culture
- Globally distributed team