Research Scientist: Post-training
United States
$200-350k + equity 
Consultant Name
Jake Pheasey
SECTOR
-JOB TYPE
Permanent
DATE POSTED
11 March 2026
Research Scientist – Post-Training
Compensation: $200-350k + equity
Location: San Francisco Bay Area (Onsite)
The founding team carries exceptional research pedigree, with alumni from some of the world's most respected AI organisations. Between the founders, they bring tens of thousands of academic citations in robotics and large-scale ML — representing some of the deepest technical credibility in the field globally.
The company's core thesis is that scaling real-world robot data, model size, and compute can unlock predictable, general improvements in robotic capability — analogous to what foundation models achieved for language and vision. The team is executing on this with both scientific rigour and real-world deployment on physical robots.
Backed by a major compute infrastructure partner alongside leading venture firms, the lab is well-resourced and operating with serious ambition. This is not incremental robotics — it's an attempt to reset what's possible.
This is where research meets reality. A high-ownership role with direct impact on how the lab's models perform in the physical world — closing the loop between model outputs and real robot behaviour.
The culture is research-driven but execution-focused: deep scientific rigour combined with a relentless focus on what actually works in the real world. It is demanding, intense, and designed for people who want to work next to the best in the field.
If you are motivated by high-impact work, deep technical ownership, and the chance to help define what general-purpose robots can do — this is worth a conversation.
Compensation: $200-350k + equity
Location: San Francisco Bay Area (Onsite)
The Company
This is a rare opportunity to join a well-funded, research-first robotics and embodied AI lab at an early but high-momentum stage — backed by tier-1 investors and strategic partners at the forefront of AI infrastructure and compute.The founding team carries exceptional research pedigree, with alumni from some of the world's most respected AI organisations. Between the founders, they bring tens of thousands of academic citations in robotics and large-scale ML — representing some of the deepest technical credibility in the field globally.
The company's core thesis is that scaling real-world robot data, model size, and compute can unlock predictable, general improvements in robotic capability — analogous to what foundation models achieved for language and vision. The team is executing on this with both scientific rigour and real-world deployment on physical robots.
Backed by a major compute infrastructure partner alongside leading venture firms, the lab is well-resourced and operating with serious ambition. This is not incremental robotics — it's an attempt to reset what's possible.
The Role
Pretraining creates generality — post-training makes it usable. As a Research Scientist focused on Post-Training, you will be responsible for adapting the company's foundation models into robust, controllable, real-world robotic systems through fine-tuning, reinforcement learning, evaluation, and on-robot validation.This is where research meets reality. A high-ownership role with direct impact on how the lab's models perform in the physical world — closing the loop between model outputs and real robot behaviour.
Responsibilities
- Design fine-tuning and adaptation strategies for downstream robotic tasks
- Improve robustness, reliability, and controllability of foundation model outputs
- Build evaluation frameworks tied directly to real robot performance metrics
- Improve inference-time characteristics including latency, stability, and memory efficiency
- Apply imitation learning, reinforcement learning, distillation, synthetic data, and curriculum learning
- Close the loop between model outputs and physical-world outcomes through on-robot validation
Key Skills & Experience
- Experience fine-tuning large models using RLHF, imitation learning, RL, distillation, or domain adaptation techniques
- Background in embodied AI, robotics, or real-world ML systems
- Strong instinct for evaluation design and systematic failure analysis
- Comfortable debugging across the full stack — from loss curves through to robot behaviour
- Enjoy rapid iteration cycles with real-world feedback
- Strong signals: top-tier research publications, prior lab experience (OpenAI, DeepMind, Anthropic or equivalent), or hands-on robotics deployment experience
Why This Role
The problems being solved here — building general-purpose embodied intelligence at scale — are among the most important and least-solved in AI today. You will have the autonomy to shape foundational research directions, work with some of the highest talent-density teams in robotics globally, and see your work deployed on real physical robots.The culture is research-driven but execution-focused: deep scientific rigour combined with a relentless focus on what actually works in the real world. It is demanding, intense, and designed for people who want to work next to the best in the field.
If you are motivated by high-impact work, deep technical ownership, and the chance to help define what general-purpose robots can do — this is worth a conversation.
Apply
Please apply via this listing or reach out directly. All applications are handled in strict confidence. The client will not be disclosed at initial stage.
