Staff MLOps Engineer

AiDASH • Palo Alto, CA

29d • Hybrid

About The Position

AiDASH is a growing company recognized for its innovative satellite and AI-powered grid protection products. The company has received multiple accolades, including being named one of America's Best Startup Employers by Forbes and a Top GreenTech Company by TIME. Deloitte Technology Fast 500™ also ranked AiDASH highly. The company is expanding its Data Inference AI Pipeline team to support aggressive customer growth and is seeking a Staff MLOps Engineer to build the operational backbone of this new team. This is a foundational, individual contributor role where the engineer will shape deployment, scaling, recovery, and cost-efficiency of AI models. The role also involves defining team culture and engineering standards. The position reports to the Director of Engineering for the Data Inference AI Pipeline team. AiDASH's pending acquisition by Schneider Electric is expected to accelerate its mission and scale. This is a hybrid role based in Palo Alto, CA, requiring two days per week in the office.

Requirements

• 7-10+ years in software/ML engineering, including 3+ years focused specifically on MLOps or ML infrastructure.
• Hands-on production experience with AWS SageMaker (or an equivalent managed ML platform) for model deployment and serving.
• A proven track record designing systems for elasticity/autoscaling, cost optimization, and failover/resilience at production scale.
• Strong expertise in containerization and orchestration (Docker, Kubernetes) and in CI/CD built for ML workloads, including model versioning and automated testing and rollout.
• Experience instrumenting and owning cost and performance metrics for the infrastructure you're responsible for.
• The ability to set technical direction as a Staff-level IC, building buy-in across Data Science and other engineering teams.
• High ownership mindset, low ego, and a collaborative spirit, with a passion for building platforms that help others ship faster.

Nice To Haves

• Experience with large imagery, video, or 3D sensor and point-cloud data pipelines or other high-volume unstructured data at scale.
• Experience with multimodal data (imagery, sensor/time-series, geospatial) beyond purely textual or tabular data.
• Experience building MLOps practices from scratch on a new team.
• Familiarity with model monitoring and drift-detection tooling, and GPU cost-optimization techniques (quantization, batching, Inferentia/Trainium or equivalent).
• A background in a regulated or enterprise B2B domain such as utilities, energy, infrastructure, healthcare, or finance.
• Relevant AWS/Kubernetes certifications or open-source MLOps tooling contributions.

Responsibilities

• Own the model deployment pipeline end-to-end: packaging, versioning, rollout, and rollback for ML models moving from training to production.
• Build and maintain deployment infrastructure on SageMaker (endpoints, batch transform, multi-model/multi-container hosting), and evaluate where alternatives such as self-hosted serving or other managed options are a better fit.
• Set the CI/CD pattern for model releases, including automated testing, staged rollout, and canary/shadow deployments.
• Design inference infrastructure that scales seamlessly with real-time and batch demand.
• Right-size compute (CPU/GPU/Inferentia or equivalent) for each model and workload to balance latency and cost.
• Load-test the pipeline and build capacity plans that stay ahead of customer growth milestones.
• Own the cost-per-inference metric: instrument it, report on it, and drive it down as volume scales.
• Build cost-awareness into architecture decisions, including autoscaling policies, spot/on-demand mix, and model size/quantization trade-offs.
• Design for graceful degradation and failover across regions and availability zones for inference serving.
• Define and test disaster-recovery procedures, and run regular failure-injection and chaos exercises.
• Build monitoring, alerting, and on-call runbooks for model-serving infrastructure, covering drift, latency SLOs, error rates, and infra health.
• Partner with Data Science to bring new models to production on a platform built to support them.
• Partner with the Web Application team where inference results power customer-facing features, keeping latency and reliability expectations aligned.
• Serve as the technical authority on MLOps practices as the Data Inference AI Pipeline team grows.

Benefits

• Comprehensive Medical, Dental, and Vision Coverage: 100% coverage for employees and 80% for their spouses and children
• Health Reimbursement Account (HRA): 100% funded by AiDASH to cover medical deductibles
• 401(k) Plan: Begin contributing after two months of employment
• Parental Leave: Supportive parental leave with 16 weeks for primary caregivers and 4 weeks for secondary caregivers
• Generous Vacation Policy: Accrue 20 vacation days per year, plus an additional flex holiday
• Winter Break: From December 25th through January 1st

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed