Member of Technical Staff, Research

Location

San Francisco, CA / Dublin, Ireland

In-person 5 days per week. Candidates may relocate from the UK to Dublin.

Compensation

$200,000 – $400,000 Base + 0.1% – 0.5% Equity

Visa

Visa sponsorship and transfers available, including OPT, H-1B transfers, new H-1B, and TN.

Company Stage of Funding

Early Stage — Founded 2024

Office Type

On-site — 5 days per week

Travel

Not specified

Company Description

We are building the infrastructure required to scale the next generation of AI training data.

As AI models progress beyond supervised fine-tuning and human-generated data toward reinforcement learning, learning from experience, synthetic data, simulation, and multimodal intelligence, access to high-quality training data is becoming an increasingly important bottleneck.

The company is focused on creating environments and datasets that enable reinforcement learning and advanced model training at scale. Its work spans human computation, synthetic data, simulation, evaluation, and RL infrastructure.

The founding team includes former ML engineers, founders, roboticists, and data leaders from leading technology and AI organizations. The team has experience deploying deep learning systems at massive scale, training state-of-the-art models for autonomous driving, and operating large-scale data pipelines involving tens of thousands of human annotators.

The team is approximately 10 people and is backed by investors and angels with backgrounds at leading AI research organizations.

What You Will Do

• Drive Foundational Research & Execution: Architect and execute a core research agenda focused on discovering simple, generalizable ideas that advance model reasoning and intelligence at scale.
• Own the full research-to-production lifecycle, moving ideas rapidly from experimentation into live systems.
• Model Alignment & Data Strategy: Partner with advanced AI research teams to design, engineer, and iterate on high-impact datasets and large-scale benchmarking programs.
• Work on critical alignment, safety, evaluation, and reward-signal problems that influence how frontier models behave.
• Autonomous Problem Selection: Independently identify, scope, and manage long-running research projects, prioritizing problems that are critical to scaling data toward AGI/ASI.
• Operate with significant autonomy and ownership over research direction, methodology, and execution.
• System Infrastructure: Collaborate closely with engineering teams on data pipelines, internal research tooling, and high-performance deep learning implementations.
• Work across research, engineering, evaluation, and productization to turn novel ideas into scalable systems.
• Investigate unfamiliar technical domains and rapidly develop enough expertise to solve highly complex problems.
• Build and improve large-scale agent systems, including orchestration frameworks, tool APIs, distributed execution, observability, and logging infrastructure.
• Contribute to high-stakes evaluation and benchmarking efforts for advanced AI systems.

Ideal Candidate Background

Experience Requirements

• 1–6 years of professional experience in AI/ML research or a closely related technical research environment.
• PhD in Computer Science, Machine Learning, NLP, or a closely related field strongly preferred.
• Exceptional research depth demonstrated through publications at venues such as NeurIPS, ICML, ICLR, ACL, or comparable conferences.
• Proven ability to take research ideas from experimentation through production deployment.
• Experience working on advanced post-training, distillation, evaluation, alignment, or reinforcement-learning methodologies.
• Strong track record of independently identifying and pursuing technically difficult research problems.
• Experience working on large-scale AI systems or research infrastructure.

Technical Requirements

• Strong Python programming skills.
• Deep understanding of machine learning and modern AI systems.
• Strong research methodology, experimentation, and analytical skills.
• Experience with model evaluation, benchmarking, and/or reward modeling.
• Experience with post-training, distillation, RL, alignment, or related model-improvement techniques.
• Ability to translate research concepts into production-quality systems.
• Strong understanding of large-scale model training or inference systems.

Agent Systems & Infrastructure Requirements

• Experience building or working with large-scale agent systems.
• Familiarity with orchestration frameworks and tool-use architectures.
• Experience with tool APIs and distributed execution.
• Strong understanding of observability, logging, and evaluation infrastructure.
• Experience designing or contributing to scalable data pipelines.
• Ability to collaborate with infrastructure and engineering teams to productionize research.

Research & Evaluation Requirements

• Experience designing rigorous experiments and benchmarks.
• Strong understanding of model evaluation methodologies.
• Experience working with high-stakes AI evaluations is highly valuable.
• Ability to identify meaningful signals from complex or noisy datasets.
• Experience designing datasets or data-generation strategies is a strong plus.
• Experience with synthetic data, simulation, reinforcement learning, or learning-from-experience systems is highly preferred.

Strategic & Ethical Leadership

• Thoughtful perspective on the societal implications of increasingly capable AI systems.
• Strong awareness of AI safety, alignment, evaluation, and governance considerations.
• Ability to reason about the risks and tradeoffs associated with deploying general-purpose AI.
• Experience contributing to AI policy, safety, or governance initiatives is a plus.

Soft Skills

• Exceptional intellectual curiosity.
• Founder mentality and extreme ownership.
• Highly autonomous and comfortable operating without predefined roadmaps.
• Ability to rapidly master unfamiliar technical domains.
• Strong written and verbal communication.
• Comfortable working at the intersection of research and execution.
• High tolerance for ambiguity and technically difficult problems.
• Strong bias toward experimentation, iteration, and shipping.
• Ability to work effectively with elite researchers and highly technical engineers.

Compensation & Benefits

• $200K – $400K base salary.
• 0.1% – 0.5% equity.
• Visa sponsorship and transfers available.
• Opportunity to work directly on frontier AI research and infrastructure.
• High-autonomy environment with significant research ownership.
• Opportunity to work alongside experienced AI researchers, ML engineers, roboticists, and founders.

Why Join

• Work at the frontier of AI research, reasoning, evaluation, and training data.
• Own research problems from initial hypothesis through production deployment.
• Work directly with leading AI research teams and advanced model-development efforts.
• Help build the data infrastructure required for the next generation of AI systems.
• Operate in a small, highly technical team where individual contributions have significant impact.
• Work on problems spanning synthetic data, simulation, RL, agents, evaluation, and model intelligence.
• Opportunity to shape foundational approaches to scaling AI training data and learning from experience.