USAMember of Technical Staff, Data Flywheel
6 дн. назад
USALeadOnsite
data evaluationdata integrationtraining datafeedback loops
Hands-on technical role focused on improving AI model performance through data collection, evaluation, and collaboration with various teams and partners.
О компании
- Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all.
Условия
- We believe that to make intelligence open and accessible to all, you need to start at the foundation. Joining Reflection means building from the ground up as part of a talent-dense team. You will help define our future as a company, and help define the future of open foundational models.
- We want you to do the most impactful work of your career with the confidence that you and the people you care about most are supported.
- Top-tier compensation: Salary and equity structured to recognize and retain our talent globally.
- Stock options: Everyone who joins and contributes to Reflection's success gets to share in the upside through stock options.
- Health & wellness: Comprehensive medical, dental, vision, and life, with an annual wellness allowance.
- Meals: Lunch and dinner are provided in the office daily.
- Life & family: 22 weeks paid parental leave for all new birthing and non-birthing parents, including adoptive and surrogate journeys.
- Vacation days: Unlimited paid time off in the U.S. and 30 days in the U.K.
- Sponsorship support: We sponsor visas to help exceptional talent join our team and support long-term immigration pathways where applicable.
- Team building: We have regular off-sites, happy hours, and team celebrations.
- Export Control Notice: This position may require access to technology or source code subject to the U.S. Export Administration Regulations. Any offer of employment for this role may be conditioned on the Company's ability to provide the candidate with access to such technology or source code in compliance with applicable U.S. export control laws, which may require the Company to seek government authorization.
Другое
- The Data Flywheel team closes the gap between benchmark performance and useful performance in the real world. We identify and build the signals, data, and feedback loops that turn model usage into rigorous evaluations, targeted training data, and measurable improvements in future generations of models.
- This is a hands-on technical role at the intersection of research and deployment. You'll take ambiguous model behaviors from first observation through measurement, intervention, and validated improvement, working across evaluation, human and synthetic data, infrastructure, post-training, and live deployments. You'll collaborate closely with researchers and engineers across the company, as well as customers, partners, vendors, and the open-source community.
- Identify high-value data sources and partnership opportunities, deeply understand the underlying use cases, and translate them into representative evaluations
- Bring new data sources online, from initial partner conversations and data scoping through quality validation and integration into production evaluation and training pipelines, where appropriate
- Design and build evaluations, graders, and feedback loops that make priority real-world model behaviors measurable
- Analyze model performance and failure modes, then translate those insights into targeted datasets, reward signals, and training interventions
- Develop human and synthetic data strategies for capabilities where existing data is insufficient, including designing and running evaluation and data-collection programs with vendors
- Build the infrastructure and pipelines needed to ingest, inspect, version, and evaluate data reliably at scale
- Collaborate closely with pre-training, post-training, applied, and partnership teams to turn new signals into measurable model improvements
- Degree (BS, MS, or PhD) in Computer Science, Machine Learning, or related discipline, or equivalent practical experience
- Deep technical understanding of LLM training and evaluation, with hands-on experience in areas such as evaluation design, data curation, reinforcement learning, or reward design
- Strong software engineering skills and experience building automated data/evaluation pipelines or large-scale ML systems
- A track record of owning high-impact projects end to end, navigating ambiguity, and adapting quickly as priorities change
- A highly collaborative, action-oriented approach and excitement about defining how a new frontier lab measures and accelerates model progress
- High agency and thrive in a fast-paced startup environment; bias for impact over process
- Enjoy collaborating across research, engineering, operations, and product disciplines