Position Summary
We are applying large language models and transformer-based architectures to problems where ground truth is delayed, noisy, and non-stationary. Our systems generate code, run experiments, and iterate autonomously, and we are looking to go beyond supervised fine-tuning.
We are hiring a Post-Training Research Scientist to build RLHF, DPO, and reward modeling capabilities from the ground up. This is a greenfield role: you will define the infrastructure, research agenda, and evaluation frameworks for aligning LLMs to sophisticated, multi-step workflows in a domain where the reward signal is fundamentally different from existing research on human preference or deterministic task completion.
This hire will help own methodology across training, fine-tuning, context management, and model evaluation. You will shape not only the post-training capability but the broader research direction of the team.
You will take on the following responsibilities:- Lead post-training efforts for LLMs applied to financial time series and quantitative reasoning
- Design and execute RLHF, DPO, and related alignment methods at scale, including deployment of substantial compute budgets (O($100mm))
- Build infrastructure for preference data collection, reward modeling, and policy optimization on financial datasets
- Drive research agenda connecting post-training methods to quantitative finance applications
- Collaborate with quant researchers to define task distributions and evaluation frameworks
- Unblock production systems dependent on post-training capabilities
You should possess the following qualifications:- BS or equivalent work experience in Science, Technology, Engineering or Math (an MS is a plus).
- Minimum 1 year of experience required; 1-10 years of experience preferred (ideally 1-5 years) at a frontier AI lab (OpenAI, Anthropic, DeepMind, Meta FAIR, or equivalent)
- Shipped post-training systems in production: RLHF, DPO, RLAIF, or related methods
- Deep understanding of distributed training infrastructure: multi-node GPU clusters, training stability, checkpointing
- Track record managing large-scale compute: budgeting, experiment design, ablations
- Publications or demonstrated expertise in alignment, preference learning, or reward modeling
- Hands-on implementation skills: PyTorch/JAX, distributed frameworks (DeepSpeed, FSDP, etc.)
You will enjoy the following benefits:- Core Benefits: Fully paid medical and dental insurance premiums for employees and dependents, competitive 401k match, employer-paid life & disability insurance
- Perks: Onsite gyms with laundry service, wellness activities, casual dress, snacks, game rooms
- Learning: Tuition reimbursement, conference and training sponsorship
- Time Off: Generous vacation and unlimited sick days, competitive paid caregiver leaves
- Hybrid Work Policy: Flexible in-office days with budget for home office setup
The base pay for this role will be between $165,000 and $300,000. This role may also be eligible for other forms of compensation and benefits, such as a discretionary bonus, health, dental and other wellness plans and 401(k) contributions. Discretionary bonus can be a significant portion of total compensation. Actual compensation for successful candidates will be carefully determined based on a number of factors, including their skills, qualifications and experience.
