Platform Engineer

Algorized is a fast-growing deep-tech startup developing cutting-edge software for human positioning and sensing. By leveraging advanced algorithms, edge ML, radar, and sensor fusion, we enable precise people tracking, positioning, vital sign detection, and age classification.

We are seeking an MLOps Engineer to design, build, and maintain scalable machine learning infrastructure for deploying models that process data from sensors, radar, and cameras. This role will be critical in ensuring high performance, real-time accuracy, and seamless ML model operations across on-premise and cloud environments. If you are resourceful, have deep understanding in system architecture, edge-computing, embedded systems and ready to join a dynamic fast-growing start-up this unique opportunity is for you!

LOCATION

Hybrid/Campbell California US

EMPLOYMENT TYPE

Full Time

ML Pipelines Development – Design, build, and maintain scalable ML pipelines to support sensor-agnostic (radar, camera) people-sensing model training, testing, deployment, and monitoring.

ML Platform Implementation – Develop a scalable ML platform integrating ML pipelines and models, transforming raw sensor data into actionable people-sensing insights.

Model Deployment – Implement efficient deployment strategies for ML models across on-premise and cloud environments, ensuring real-time inference and seamless scalability.

Automation, CI/CD & Observability

End-to-End Pipeline Management – Optimize ML workflows for real-time sensor-driven applications, ensuring robust model lifecycle management.

CI/CD & Automation – Design and optimize CI/CD pipelines to automate model integration, retraining, validation, and deployment.

Observability & Performance Monitoring – Implement monitoring tools (e.g., Prometheus, Grafana, Kibana) to track model accuracy, performance, and scaling issues.

Infrastructure, Security & Scaling

Containerization & Orchestration – Deploy and scale ML services using Docker and Kubernetes across edge and cloud environments.

Infrastructure & Security Best Practices – Implement cloud-native best practices for infrastructure automation, logging, alerting, and security monitoring.

Collaboration & Strategic Impact

Cross-Functional Collaboration – Work closely with data scientists, software engineers, and product teams to define objectives, deliver key milestones, and align solutions with business needs.

Technology Roadmap Contribution – Play a key role in shaping and executing Algorized’s AI-powered sensing platform roadmap.

MSc or advanced degree in a relevant field with 5+ years of experience in MLOps, ML model deployment, and cloud infrastructure.

Strong hands-on experience with deployment and scaling of ML models on on-premise and cloud architectures.

Expertise in sensor fusion, edge AI, or embedded ML models for real-time applications.

Knowledge of AI agent architectures or Reinforcement Learning concepts.

Ability to design efficient communication protocols between front-end and back-end systems.

API & Communication Protocol Development

– Experience with RESTful APIs, Web Sockets, Graph

QL.

Database & Real-Time Data Management – Strong understanding of database design, real-time data ingestion, and efficient storage solutions.

Experience with time-series data, sensor data pipelines, or stream processing frameworks.

Deep expertise in Docker, Kubernetes, and modern CI/CD practices.

Strong problem-solving & debugging skills, with the ability to design scalable, efficient systems.

Excellent teamwork & communication skills, with a passion for innovation and solving complex challenges.

Willingness to travel domestically and internationally for development and on-site customer support.

Preferred Requirements

Experience with observability tools (e.g., Grafana, Prometheus, Kibana, Elasticsearch).

Knowledge of event-driven architectures (e.g., Kafka, MQTT, Pub/Sub).

Experience in real-time AI model inference & post-deployment monitoring.

Familiarity with 3D positioning, tracking systems, or vital signs estimation.