Remote Artificial Intelligence Engineer

We are seeking a talented Artificial Intelligence Engineer to join our remote team and drive the development of cutting-edge AI solutions. In this role, you will design, build, and deploy machine learning models and AI-powered features that solve real-world problems at scale. You will collaborate closely with product managers, data scientists, and software engineers to translate business requirements into robust, production-ready AI systems.

Key Responsibilities:

• Research, prototype, and implement state-of-the-art machine learning algorithms (deep learning, reinforcement learning, NLP, computer vision).
• Build and maintain scalable ML pipelines for data ingestion, training, evaluation, and deployment.
• Optimize models for performance, latency, and cost-effectiveness in cloud environments (AWS, GCP, Azure).
• Conduct rigorous experimentation, A/B testing, and monitoring to ensure model reliability and fairness.
• Contribute to the AI strategy and roadmap, identifying opportunities for innovation and competitive advantage.
• Mentor junior engineers and promote best practices in ML engineering, MLOps, and responsible AI.

What We Offer:

• Competitive salary range of $200,000 – $270,000 (depending on experience).
• Fully remote work with flexible hours.
• Comprehensive health, dental, and vision benefits.
• Generous equity package and 401(k) matching.
• Annual learning budget for conferences, courses, and certifications.
• Collaborative, inclusive culture that values diversity and work-life balance.

If you are passionate about pushing the boundaries of AI and thrive in a fast-paced, remote-first environment, we’d love to hear from you.

• Education: MS or PhD in Computer Science, Machine Learning, Statistics, or related quantitative field (or equivalent industry experience).
• Experience: 5+ years of professional experience building and deploying ML models in production.
• Technical Skills:
  • Expert proficiency in Python and major ML frameworks (PyTorch, TensorFlow, JAX).
  • Strong background in deep learning architectures (Transformers, CNNs, RNNs, GNNs).
  • Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow, Docker, Kubernetes).
  • Solid understanding of cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML).
  • Familiarity with large-scale data processing (Spark, Ray, Dask) and feature stores.
• Soft Skills: Excellent communication, ability to translate complex technical concepts to non-technical stakeholders, and a track record of driving projects from ideation to delivery.
• Nice to Have: Publications in top-tier ML venues (NeurIPS, ICML, ICLR, CVPR), open-source contributions, experience with generative AI / LLMs.