Applied Research Engineer - San Francisco, CA

Applied Research Engineer AI Alignment & Human Feedback Systems in San Francisco, CA This role is offering sponsorship - open to H1B transfers but not new sponsorships Shape the Future of AI Our clients' company is building the critical infrastructure that powers breakthrough AI models for top research labs and enterprise teams. Since 2018, they have been pioneering data-centric approaches essential to AI development. As AI capabilities expand exponentially, their work becomes even more vital. They are the only company offering three seamlessly integrated solutions for frontier AI development:

• Enterprise Platform & Tools Advanced annotation tools, workflow automation, and quality control systems enabling high-quality training data at scale
• Frontier Data Labeling Service Expert-driven data labeling, using proprietary systems and subject matter experts to support next-generation AI models
• Expert Marketplace - A dynamic network of skilled annotators and domain experts to flexibly scale AI training pipelines

Why Join Us

• High-Impact Environment: Operate in a fast-paced, startup-style environment where impact trumps process. You will grow quickly and take on real responsibility.
• Technical Excellence: Collaborate with some of the sharpest minds in AI, working on challenges at the bleeding edge of machine learning and human-AI collaboration.
• Innovation at Speed: They reward ownership, initiative, and velocity. Make things happen fast and make them matter.
• Continuous Growth: Surround yourself with intellectually curious peers and stay ahead of the AI curve through constant learning and experimentation.
• Clear Ownership: Know what you are accountable for and have the autonomy to deliver with purpose.

Role Overview As an Applied Research Engineer, you'll design and build advanced systems to collect, analyze, and optimize human-in-the-loop data for training cutting-edge AI models. Your work will focus on techniques such as Reinforcement Learning from Human Feedback (RLHF), Direct Preference Optimization (DPO), and novel feedback mechanisms to ensure that frontier models align with human values and preferences. This is a unique opportunity to blend research, engineering, and human-centered design to shape the next generation of AI systems. What You Will Do

• Develop state-of-the-art methods for aligning AI systems with human intent using techniques like RLHF and beyond.
• Design systems to rigorously measure and improve the quality of human feedback used in AI training.
• Build tools to enhance data labeling processes through AI-assisted workflows, active learning, and adaptive sampling.
• Investigate the impact of different types of feedback e.g., demonstrations, critiques, comparison model performance and behavior.
• Create algorithms to optimize how AI learns from humans, improving adaptability and safety.
• Translate research breakthroughs into practical, scalable tools that integrate directly into production workflows.
• Publish and present your work at top-tier ML/AI venues and actively engage with the broader AI research community.
• Help define best practices and contribute to the evolution of industry standards in human-AI alignment.

What You Bring

• Ph.D. or Masters in Computer Science, Machine Learning, AI, or related field.
• 3+ years of experience solving complex ML problems with real-world impact.
• Deep knowledge of frontier model training, data-centric AI, and alignment techniques.
• Strong expertise in building systems for human data quality measurement and optimization.
• Proficiency in Python and frameworks such as PyTorch, JAX, or TensorFlow.
• A publication record at top-tier conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, etc.).
• Ability to rapidly prototype, test, and iterate research ideas into working systems.
• Excellent analytical thinking, problem-solving skills, and a strong bias toward action.
• Comfortable collaborating across multidisciplinary teams and clearly communicating complex ideas.

Were committed to redefining what it means for AI to learn from humans. Our research spans machine learning, human-computer interaction, and AI ethics ensuring real-world applicability, transparency, and responsibility in every system we build. You will join a team that values curiosity, rigor, and a deep passion for pushing the boundaries of what is possible in AI. Open to H1B transfers but not new sponsorships.