Remote Artificial Intelligence Engineer – Worldwide – Shape the Future of Intelligent Systems

Join a globally distributed team building cutting‑edge AI solutions that power real‑world products. We offer a fully remote environment, competitive compensation, and the freedom to innovate from anywhere.

About the Role

As an Artificial Intelligence Engineer you will design, train, and deploy machine learning models that solve complex business problems across multiple domains. You will collaborate with data scientists, product managers, and software engineers to turn research prototypes into scalable production services. This position is 100 % remote, allowing you to work from any location with a reliable internet connection.

Key Responsibilities

• Develop end‑to‑end ML pipelines — data ingestion, feature engineering, model training, validation, and deployment.
• Research and implement state‑of‑the‑art algorithms (transformers, diffusion models, reinforcement learning, etc.) tailored to product needs.
• Optimize models for latency, throughput, and cost on cloud platforms (AWS, GCP, Azure) and edge devices.
• Build automated monitoring, retraining, and A/B testing frameworks to maintain model health in production.
• Contribute to open‑source tooling and internal libraries that accelerate experimentation.
• Mentor junior engineers and promote best practices in reproducible research and responsible AI.

Requirements

Technical Expertise

• 3+ years of professional experience building production‑grade machine learning systems.
• Deep proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, JAX).
• Strong grasp of distributed training, model quantization, ONNX export, and serving stacks (TorchServe, Triton, TensorFlow Serving).
• Experience with MLOps tools: MLflow, Kubeflow, Airflow, or custom CI/CD for models.
• Solid understanding of data engineering concepts — SQL, Spark, Parquet, feature stores.

Soft Skills & Mindset

• Ability to translate ambiguous product requirements into clear technical specifications.
• Excellent written and verbal communication for asynchronous, cross‑time‑zone collaboration.
• Growth‑oriented mindset; comfortable reading papers and rapidly prototyping ideas.
• Commitment to ethical AI — bias detection, fairness metrics, and privacy‑preserving techniques.

Preferred Qualifications

• Published research at top conferences (NeurIPS, ICML, CVPR, ACL) or a strong portfolio of open‑source contributions.
• Experience with large‑scale language models, multimodal architectures, or generative AI.
• Familiarity with Kubernetes, Terraform, and GitOps for infrastructure as code.
• Background in a regulated industry (healthcare, finance, autonomous vehicles) where model governance is critical.

Salary Range

$130,000 – $180,000 USD per year, commensurate with experience and geographic cost‑of‑living adjustments.

Benefits

• Full‑time remote work with a generous home‑office stipend ($2,500 annually).
• Comprehensive health, dental, and vision coverage for you and dependents.
• Unlimited PTO plus 10 paid company holidays.
• Annual learning budget ($3,000) for courses, conferences, and certifications.
• Equity grant with a 4‑year vesting schedule.
• Quarterly team retreats (virtual or in‑person) to strengthen culture and alignment.

Culture & Values

Our engineering culture prizes transparency, data‑driven decision making, and psychological safety. We run blameless post‑mortems, hold weekly “demo days” to showcase progress, and encourage engineers to allocate 20 % of their time to exploratory research. Diversity of thought is not a buzzword — it’s a hiring criterion and a daily practice.

How to Succeed in This Role

Success means delivering models that move key product metrics while keeping technical debt low. You will thrive if you enjoy end‑to‑end ownership, love sharing knowledge through documentation and pair‑programming, and can balance rapid experimentation with production rigor. The most impactful engineers here treat every experiment as a potential product feature and every production incident as a learning opportunity.