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About This Role
Who We Are
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Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end\-to\-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction.
Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer\-first software with cost\-efficient, large\-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in.
We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute.
What We're Looking For
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Lightning AI is looking to hire an AI Platform Support Engineer to join our US Customer Experience team, supporting ML engineers running large\-scale training and inference workloads across cloud infrastructure, Kubernetes, and GPU platforms in production environments.
This role sits at the intersection of ML systems, cloud infrastructure, Kubernetes, and customers. You'll support engineers training models, deploying inference systems, and scaling GPU workloads in production.You are not a ticket router or traditional support engineer. You are a technical partner to ML teams \- helping diagnose failures, improve reliability, and guide customers through complex distributed systems problems.
The problems range from Kubernetes scheduling and GPU orchestration to distributed PyTorch failures, inference latency, networking bottlenecks, storage performance, and platform reliability. You'll gain exposure to a wide variety of real world AI workloads across industries and help shape the infrastructure powering the next generation of ML applications.
What You'll Do
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Work Directly With ML Engineers
- Partner directly with customer engineering teams running training and inference workloads in production
- Help customers diagnose and resolve complex distributed systems and ML infrastructure issues
- Act as a technical advisor during high impact incidents and platform degradation events
- Translate infrastructure level issues into actionable guidance for ML engineers
- Build credibility with customers through strong technical reasoning and clear communication
Debug ML Infrastructure \& Distributed Workloads
- Investigate failures involving distributed training, Kubernetes orchestration, GPU allocation, networking, and storage systems
- Troubleshoot PyTorch, CUDA, NCCL, and inference serving related issues
- Analyze logs, metrics, traces, and system behavior to isolate root causes
- Debug containerized workloads running across Kubernetes and bare metal GPU environments
- Support customers scaling workloads across multi node GPU systems
- Diagnose performance bottlenecks involving compute, memory, networking, or storage
Improve Reliability \& Platform Operations
- Identify recurring patterns across customer issues and drive long term reliability improvements
- Contribute to post incident reviews and operational improvements
- Build internal tooling, automation, documentation, and runbooks
- Partner closely with infrastructure, networking, and platform engineering teams
- Help improve observability, operational visibility, and troubleshooting workflows
- Improve the customer experience through better processes and technical guidance
What This Role Is Not
To set clear expectations:
- This is not a traditional help desk or ticket routing support role
- This is not purely customer success or account management
- This is not a backend engineering role
- This is not a passive escalation position
This role is for engineers who enjoy solving difficult technical problems while working closely with other engineers.
What You'll Need
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### Required Qualifications
#### Infrastructure \& Systems
- Strong software engineering and systems troubleshooting background
- Experience with Kubernetes and containerized environments
- Linux systems knowledge, including networking, storage, process management, and performance tuning
- Experience with cloud infrastructure and distributed systems
- Experience with observability and debugging tools such as Prometheus, Grafana, or OpenTelemetry
#### ML Infrastructure Experience
- Hands on experience operating machine learning workloads in production or research environments
- Experience with distributed ML systems and tooling such as PyTorch, CUDA, or NCCL
- Familiarity with GPU infrastructure and orchestration
- Experience troubleshooting performance, reliability, or scaling issues in ML infrastructure
- Understanding of the operational challenges involved in running ML systems at scale
#### Collaboration
- Strong communication skills and ability to work directly with highly technical customers and engineering teams
- Comfortable operating in fast moving, highly ambiguous environments
- Enjoys solving complex technical problems collaboratively
### Ideal Experience
- Experience with large scale model training or distributed inference systems
- Familiarity with Ray, Kubeflow, Slurm, or similar distributed scheduling platforms
- Experience with InfiniBand, RDMA, or high\-performance networking
- Experience operating bare metal infrastructure
- Familiarity with storage systems commonly used in ML environments
- Experience working at an AI infrastructure, cloud, MLOps, or developer tooling company
- Contributions to platform engineering, developer infrastructure, or operational tooling projects
- Experience writing automation, tooling, or scripts in Python or similar languages
*This role is hybrid out of our Seattle or San Francisco offices, with an in\-office requirement of at least 2 days per week and occasional team and company offsites. The role follows a Monday–Friday schedule, with working hours from 8:00 AM to 5:00 PM PST. We are not able to provide visa sponsorship for this role at this time.*
Benefits and Perks
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We offer a comprehensive and competitive benefits package designed to support our employees' health, well\-being, and long\-term success. Benefits may vary by location, team, and role.
Benefits include:
- Comprehensive medical, dental and vision coverage (U.S.); Private medical and dental insurance (U.K.)
- Retirement and financial wellness support (U.S.); Pension contribution (U.K.)
- Generous paid time off, plus holidays
- Paid parental leave
- Professional development support
- Wellness and work\-from\-home stipends
- Flexible work environment
*At Lightning AI, we are committed to fostering an inclusive and diverse workplace. We believe that diverse teams drive innovation and create better products. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We are dedicated to building a culture where everyone can thrive and contribute to their fullest potential.*
Salary Context
This $115K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Lightning AI, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($127K) sits 30% below the category median. Disclosed range: $115K to $140K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Lightning AI AI Hiring
Lightning AI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $140K - $140K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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