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About This Role
About AiDASH
AiDASH is leading the PreventionFirst™movement for electric utilities and transforming grid resilience through its pioneering platform that unifies vegetation, asset, storm, and wildfire intelligence. Powered by SatelliteFirst™ Inspection & Monitoring, AiDASH delivers comprehensive visibility across the entire grid at the right frequency and budget, using the right data modality. More than 200 customers trust AiDASH to keep the lights on, spend where it counts, and defend every decision, Securing Tomorrow across every mile of the grid. Learn more at www.aidash.com.
The PreventionFirst movement is growing, and so is the recognition behind it. In 2026, Forbes named AiDASH one of America's Best Startup Employers for the 4th consecutive year, and TIME included AiDASH among America's Top GreenTech Companies for the 3rd year in a row. Deloitte Technology Fast 500™ ranked AiDASH No. 12 in the San Francisco Bay Area, and No. 59 overall in their selection of the top 500 for 2024.
Join us in Securing Tomorrow Together!
The Role
Our satellite and AI-powered products are changing how utilities see and protect the grid, and we're gearing up for aggressive customer growth over the next two years. That growth runs on AI at scale. We're building a brand-new Data Inference AI Pipeline team to deliver it, and we're looking for a Staff MLOps Engineer to architect its operational backbone from the very first line of infrastructure code.
As the very first engineer on this team, you'll start with a blank canvas and a rare chance to build something that lasts. You'll shape how our models are deployed, how the pipeline scales and recovers under load, and what it costs to run. Just as importantly, you'll help define the culture, engineering standards, and ways of working that every future teammate inherits. This is a hands-on, high-leverage individual contributor role: your decisions will set the foundation the entire team builds on, and you'll be the go-to technical authority on MLOps as the team grows. You'll report to the Director of Engineering for the Data Inference AI Pipeline team.
Our pending acquisition by Schneider Electric will accelerate our mission as we bring our combined offering to our joint customer base, and the infrastructure you build will power outcomes at an even greater scale.
Location: This is a hybrid role based in Palo Alto, CA, requiring 2 days per week in the office.
How you'll make an impact:
Deployment
- 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
Scalability & elasticity
- 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
Cost
- 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
Failover & resilience
- 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
Cross-team leadership
- 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
What success looks like in your first 6 months:
- Model deployment is a repeatable, automated process across models
- Inference infrastructure has proven it scales up and down smoothly under real load
- Cost-per-inference is measured, visible, and trending down as volume grows
- A tested failover and disaster-recovery plan is in place for the inference pipeline, with at least one failure-injection exercise completed
- Data Science can ship new models into production quickly and independently
What we're looking for:
Minimum Qualifications
- 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
Preferred Qualifications
- 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
What you'll love:
- 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 to prepare for your future. Currently, no company match is offered
- 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 enjoy an additional flex holiday to celebrate whatever feels most important to you
- Winter Break: From December 25th through January 1st, we give everyone time off to recharge and enjoy time with family and friends!
We are proud to be an equal-opportunity employer. We are committed to embracing diversity and inclusion in our hiring practices, and we promote a work environment where everyone, from any race, color, religion, sex, sexual orientation, gender identity, or national origin, can do their best work.
We offer a competitive annual pay range of $230,000 to $270,000 for this full-time position, which includes a base salary and bonus based on performance. This range reflects the anticipated annual pay range (base + bonus) for new hires. We strive to ensure our compensation packages are equitable and aligned with industry standards. Your recruiter can share more about compensation during the hiring process.
We are committed to providing an inclusive and accessible interview experience for all candidates. Please let us know if you require any accommodation during the interview process, and we will make every effort to meet your needs.
Read our Privacy Policy here: https://www.aidash.com/policy/privacy-policy/
Salary Context
This $230K-$270K range is above the 75th percentile for MLOps Engineer roles in our dataset (median: $172K across 12 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 9,079 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At AiDash, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $204,000 based on 162 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($250K) sits 23% above the category median. Disclosed range: $230K to $270K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
AiDash AI Hiring
AiDash has 2 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer. Positions span Palo Alto, CA, US, San Jose, CA, US. Compensation range: $270K - $330K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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