Machine Learning Systems Engineer, Model Evaluations

As a Machine Learning Systems Engineer on the Model Evaluations team at Anthropic, your primary responsibilities include designing, building, and maintaining the Model Evaluations infrastructure. You'll develop APIs specifically for Research Inference, create data pipelines for evaluating models, and ensure the systems are optimized for reliability and performance. Collaboration with research teams to understand their needs and translating these into technical solutions will be a crucial part of your role.

Anthropic requires candidates for the Machine Learning Systems Engineer position to have at least 5 years of software engineering experience, with proficiency in Python and familiarity with cloud services like AWS or GCP. A Bachelor's degree in a related field or equivalent experience is also necessary. While past experience in machine learning is appreciated, strong software engineering skills can also be enough to qualify for this exciting opportunity!

As a Machine Learning Systems Engineer at Anthropic, collaboration is key! You'll work closely with research teams to fully understand their workflows and challenges. Your role will involve translating their needs into practical, reusable infrastructure that enhances their evaluation processes, making their work more efficient and reproducible.

Yes, Anthropic does offer visa sponsorship for the Machine Learning Systems Engineer role. While not every candidate will qualify for sponsorship, the company is committed to making every reasonable effort to assist in obtaining a visa for successful candidates, including retaining an immigration lawyer to navigate the process.

Anthropic distinguishes itself through its commitment to big science in AI research, focusing on impactful large-scale projects rather than smaller, isolated tasks. The team emphasizes collaboration, ensuring that every engineer's voice is heard and driving forward research that aligns with ethical and societal implications of AI. This makes Anthropic an attractive choice for Machine Learning Systems Engineers looking to make a meaningful difference in the field!

When discussing a complex ML project, structure your response by outlining the problem statement, your role, the technologies used, and the impact of your work. Highlight your problem-solving techniques and collaboration with team members to convey your teamwork and technical capabilities.

In your answer, highlight your experience with tools like TensorFlow, PyTorch, or Scikit-learn, and explain why you prefer them for scalability. Discuss your familiarity with cloud platforms, Kubernetes, or data processing tools that facilitate building efficient ML systems.

Share your methodical approach to optimizing model evaluations—whether it's through hyperparameter tuning, adjusting architectural elements, or leveraging infrastructure for efficiency. Discuss how you measure performance improvements and ensure the evaluations are reliable.

Effective communication is essential. Discuss your strategies, such as regular check-ins, using feedback loops, and collaborative tools to keep research teams updated and engaged in the project’s progress, ensuring everyone is aligned.

Detail your experience with developing and optimizing APIs for machine learning applications. Explain specific projects where you built APIs, integrating them into bigger systems, and how they facilitated model access and usability for researchers.

When talking about a challenge, focus on a specific instance where you encountered significant roadblocks—describe the situation, your approach to resolving it, and the outcome. This showcases your problem-solving and critical thinking skills.

Explain your thoughts on scalability, data integrity, and data processing efficiency. Discuss how you ensure that the pipelines can handle various data sources and adapt to new requirements as research needs evolve.

Discuss specific metrics you use to evaluate model performance such as accuracy, precision, recall, or F1 score, depending on the context of the ML system. Mention any evaluation frameworks you are familiar with.

Describe any relevant experience you have with real-time inference systems. Discuss the systems you've worked with, the challenges they posed, and how you managed latency and throughput to ensure smooth operations.

Highlight your commitment to thorough documentation—how you document APIs, system architecture, user guides, and troubleshooting steps. Explain your approach to making documentation easily accessible and understandable for users.