ML Engineer, AI Safety

The impact youll make

• Enable rapid, responsible releases by coding evaluation pipelines that surface safety issues before models reach production.
• Drive operational excellence through reliable, secure, and observable systems that safety analysts and customers trust.
• Advance industry standards by contributing to bestpractice libraries, opensource projects, and internal frameworks for testing alignment, robustness, and interpretability.

What youll do

• Design & implement safety toolingfrom automated adversarial test harnesses to driftmonitoring dashboardsusing Python, PyTorch/TensorFlow, and modern cloud services.
• Collaborate across disciplines with fellow engineers, data scientists, and product managers to translate customer requirements into clear, iterative technical solutions.
• Own quality endtoend: write unit/integration tests, automate CI/CD, and monitor production metrics to ensure reliability and performance.
• Containerize and deploy services using Docker and Kubernetes, following infrastructureascode principles (Terraform/CDK).
• Continuously learn new evaluation techniques, model architectures, and security practices; share knowledge through code reviews and technical talks.

Experiences youll bring

• 3+ years of professional software engineering experience, ideally with dataintensive or MLadjacent systems.
• Demonstrated success shipping production code that supports highavailability services or platforms.
• Experience working in an agile, collaborative environment, delivering incremental value in short cycles.

Technical skills you'll need

• Languages & ML frameworks: Strong Python plus handson experience with PyTorch or TensorFlow (bonus points for JAX and LLM finetuning).
• Cloud & DevOps: Comfortable deploying containerized services (Docker, Kubernetes) on AWS, GCP, or Azure; infrastructureascode with Terraform or CDK.
• MLOps & experimentation: Familiar with tools such as MLflow, Weights & Biases, or SageMaker Experiments for tracking runs and managing models.
• Data & APIs: Solid SQL, exposure to at least one NoSQL store, and experience designing or consuming RESTful APIs.
• Security mindset: Awareness of secure coding and compliance practices (e.g., SOC 2, ISO 27001).

Nice to have: LangChain/LangGraph, distributed processing (Spark/Flink), or prior contributions to opensource ML safety projects.

Why youll love this role

• Enjoy the freedom to work from anywhere your productivity, your environment
• Purpose-driven work: Contribute meaningfully to a safer AI future while enabling groundbreaking innovation.
• Strategic visibility: Directly impact high-profile AI safety initiatives with industry-leading customers.
• Growth opportunities: Collaborate with top-tier AI talent and help shape an emerging industry.
• Supportive culture: Enjoy competitive compensation, flexible work arrangements, and significant investment in your professional and personal growth.