AI Research Engineer

Job not on LinkedIn

August 22

Apply Now

Receive Emails with Similar Jobs

Weekday (YC W21)

WebsiteLinkedInAll Job Openings

Human Resources • SaaS • Recruitment

Weekday is a modern recruitment platform that combines AI technologies with a vast database of potential candidates, aiming to streamline the hiring process for companies in India. They offer various services, including a proactive outreach approach that helps employers connect with top talent, as well as tools for candidates to easily apply for jobs. Weekday's emphasis on candidate engagement through multiple channels, including email, WhatsApp, and phone calls, sets it apart in the competitive landscape of recruitment agencies.

11 - 50 employees

Founded 2021

☁️ SaaS

🎯 Recruiter

📋 Description

• This role is for one of the Weekday's clients. • Min Experience: 6 years. • As an AI Research Engineer, you will be responsible for designing, developing, and deploying advanced AI/ML models, contributing to fundamental research, and translating theoretical breakthroughs into practical, scalable systems. You will collaborate with cross-functional teams of data scientists, engineers, and product managers to drive innovation and shape the future of our AI-driven products and services.

🎯 Requirements

• Conduct advanced research in machine learning, natural language processing (NLP), computer vision, reinforcement learning, generative AI, and other emerging AI domains. • Design, develop, and optimize algorithms and models that push the state-of-the-art in AI and enable innovative product features. • Translate research insights into real-world applications, ensuring scalability, efficiency, and robustness of AI solutions. • Publish research papers, file patents, and contribute to the scientific community while driving innovation internally. • Collaborate with engineering teams to integrate AI models into production systems, ensuring seamless deployment and monitoring. • Evaluate and benchmark new tools, frameworks, and methodologies to maintain competitive advantage in AI innovation. • Mentor junior engineers and researchers, guiding them in research practices, experimentation, and model development. • Stay ahead of advancements in AI, leveraging new architectures, optimization techniques, and compute-efficient methods for large-scale AI systems. • Experience: 6–15 years of proven expertise in AI/ML research and engineering roles, ideally within product-based or research-driven organizations. • Strong academic background in Computer Science, Artificial Intelligence, Machine Learning, or a related discipline. A PhD or Master’s degree is highly preferred. • Hands-on experience with deep learning frameworks such as TensorFlow, PyTorch, or JAX. • Strong foundation in algorithms, data structures, statistics, optimization, and probability theory. • Proven experience in developing and deploying advanced machine learning models in areas such as: • Natural Language Processing (NLP): LLMs, transformers, embeddings, semantic search, question answering. • Computer Vision: CNNs, object detection, image generation, multimodal learning. • Generative AI: Diffusion models, GANs, reinforcement learning with human feedback (RLHF). • Reinforcement Learning & Autonomous Systems: Policy optimization, decision-making under uncertainty. • Experience working with large-scale datasets, distributed systems, and cloud platforms (AWS, GCP, Azure). • Strong coding proficiency in Python, with experience in C++/Java being a plus. • Track record of publications in top-tier AI/ML conferences or journals (NeurIPS, ICML, CVPR, ACL, AAAI, etc.) is highly desirable. • Excellent problem-solving, analytical thinking, and communication skills. • Passion for advancing the state-of-the-art in AI and applying research to solve real-world challenges. • Ability to work in a fast-paced, innovation-driven environment with minimal supervision. • Demonstrated leadership in driving AI research initiatives or leading small research teams. • Curiosity-driven mindset with the ability to bridge theoretical concepts and practical engineering.