Machine Learning Operations

Location: Menlo Park, CA

Department: Engineering

Location Type: IN_OFFICE

Employment Type: FULL_TIME

• Design, build, and maintain scalable ML pipelines for training, evaluation, and deployment of LLMs and retrieval-augmented systems, optimized for performance, traceability, and reproducibility
• Operationalize evaluation workflows using both synthetic and human-labeled datasets to monitor model quality at scale across multiple downstream tasks and customer deployments
• Automate the ML Developer lifecycle by implementing robust data versioning, model tracking, and CI/CD pipelines using modern ML Ops tooling
• Optimize model training and inference, focusing on reducing latency, maximizing throughput, and controlling cost across heterogeneous hardware environments.
• Collaborate cross-functionally with research, infrastructure, and product teams to productionize foundation models and integrate them into customer-facing AI products
• Deploy and manage both open-source and proprietary models within stringent constraints on latency, security, and compliance—balancing reliability with innovation.
• Implement real-time monitoring and alerting systems to detect model/data drift, quality regressions, and infrastructure bottlenecks in live environments.
• Work directly with enterprise customers, supporting deployment strategies, ensuring production readiness, and creating tight feedback loops from real-world usage to continuous model improvement

• Software Engineering Expertise: Proven experience in building reliable and scalable systems, with a strong foundation in software engineering principles and expertise with Python, Go, or Rust
• ML Ops Platforms: Hands-on experience with ML Ops platforms such as MLflow, Kubeflow, SageMaker, Vertex AI, or Apache Airflow, facilitating efficient model lifecycle management
• Cloud-Native Tools: Proficiency in cloud-native tools including Docker, Kubernetes, storage optimizers. Experience with major cloud providers like AWS, GCP or other compute providers for deploying and managing ML workloads with a focus on cost optimization
• Experiment Tracking & Model Management: Proficiency in tools like Weights & Biases, MLflow, and/or CometML for tracking experiments, managing model metadata, and facilitating collaboration
• Infrastructure: Familiarity with infrastructure tools like Terraform, Pulumi, and Chronosphere for monitoring and alerting.
• Demonstrated ability to translate research findings into robust, production-ready systems, bridging the gap between experimentation and deployment.

• Some background in hardware/electronics, gained through professional, academic, or personal projects
• Contributions to open-source initiatives
• Notable awards or publications in leading journals/conferences
• Experience thriving in a fast-paced, hyper-growth startup environment

• Unlimited PTO: Recharge when you need it, no questions asked.
• Comprehensive Health Coverage: Medical, dental, and vision insurance for you and your dependents. 
• Free Meals and Snacks: Daily lunches, dinners, and snacks in the office.
• Professional Growth: We invest in your continuous learning and offer opportunities to expand your skills.
• Visa Sponsorship: We welcome global talent and provide visa sponsorship to support qualified candidates.