Applied Research Engineer
About this role
Join the Future of Human-Centered AI
Were on a mission to build the critical infrastructure powering the next generation of AI. Since 2018, weve led the charge in data-centric AI developmentcombining top-tier tools, expert data labeling, and scalable human feedback systems to shape frontier models.
Our three integrated solutions fuel AI innovation:
Enterprise Platform & Tools: Advanced annotation tools and workflow automation for high-quality training data at scale.
Frontier Data Labeling Services: Expert-driven labeling for next-gen AI systems.
Expert Marketplace: A flexible, on-demand network of specialized annotators and domain experts.
Why Join Us
High Impact: Work at a fast-paced, mission-driven company where your contributions matter.
Cutting-Edge Innovation: Tackle real challenges in AI alignment and human feedback systems.
Collaborative Excellence: Join a team of top researchers and engineers passionate about ethical AI.
Growth & Learning: Continuously learn, experiment, and grow your career in a high-caliber environment.
Clear Ownership: Take initiative, lead with autonomy, and see your work in production.
What Youll Do
Develop novel methods to align AI systems with human intent using RLHF, DPO, and related approaches.
Design and build tools for collecting, evaluating, and optimizing human feedback in AI training.
Enhance data labeling pipelines through active learning, adaptive sampling, and AI-assisted workflows.
Analyze feedback types (demonstrations, comparisons, critiques) to improve model performance and safety.
Translate research into scalable, production-ready systems and contribute to platform innovation.
Publish at top ML/AI conferences and help shape industry best practices in alignment research.
What You Bring
Masters or Ph.D. in Computer Science, Machine Learning, or related field.
3+ years of experience applying ML to real-world problems, preferably in alignment or human-in-the-loop systems.
Deep understanding of data-centric AI, frontier model training, and feedback optimization.
Proficiency in Python and ML frameworks (e.g., PyTorch, JAX, TensorFlow).
A track record of research publications (NeurIPS, ICML, ICLR, ACL, etc.) is a strong plus.
Strong prototyping, problem-solving, and analytical thinking skills.
Excellent communication and a collaborative mindset.
Our Mission
Were redefining how AI learns from humans. By combining machine learning, human-computer interaction, and ethical AI research, we ensure every system we build is responsible, effective, and aligned with real-world human values.
Originally posted on Himalayas
