Machine Learning Operations

About Voltai Voltai is the leading AI company building agentic systems and frontier foundation models for semiconductor and electronics design. Backed by Sequoia Capital, we’re putting AI in the hands of hardware engineers in over 70% of the world’s largest semiconductor and electronics companies to have effortless control over their next-generation chip and board designs, powering the future of automotive, industrial automation, consumer electronics, IoT, and semiconductor manufacturing. About the Team Our founding team consists of IOI/IPhO olympiad medalists, Stanford professors, ex-CTO of Synopsys, and our business leadership has scaled revenue in their previous companies to over $1.5bn. At Voltai, we are combining the world’s best talent in the intersection of software and hardware. Key Responsibilities 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 Required Skill Sets 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. Bonus Points 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 Our Benefits 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.