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About This Role
Who We Are
Physical Intelligence is bringing general-purpose AI into the physical world. We are a team of engineers, scientists, roboticists, and company builders developing foundation models and learning algorithms to power the robots of today and the physically-actuated devices of the future.
The Team
The Infrastructure team builds and operates the backbone of everything PI does: from training state-of-the-art VLA models, to orchestrating large-scale simulation, to reliably deploying intelligence across fleets of physical robots. The team works closely with researchers, robotics runtime, product, and platform engineers to ensure infrastructure scales from prototype to production-grade deployments.
In This Role You Will
- Own and scale AI-native infrastructure: You will operate and evolve Kubernetes clusters and service deployment patterns, and help build a scalable microservice platform for internal systems such as evaluation services, operational tooling, and internal APIs with agent-use as the primary interface. This includes supporting safe rollouts, upgrades, and rollback strategies.
- Drive security, observability and cost-aware infrastructure: You will treat security, logging, metrics, tracing, and alerting as first-class platform primitives, and build systems that surface reliability and performance issues early. You will also help improve cost visibility and enable cost-aware decision-making at the infrastructure level.
- Harden platform foundations: You will own core infrastructure with multi-cloud considerations, designing authentication/authorization flows, networking architecture, quota and rate-limiting services, and cloud primitives that behave predictably. A major part of this work is reducing infra churn by standardizing patterns, abstractions, and interfaces.
- Improve developer experience: You will build clear, documented interfaces for using platform infrastructure, reducing the gap between “I need infra” and “I can run my workload.” This includes supporting consistent local vs. remote development workflows and improving self-serve infrastructure usage.
- Collaborate and lead through ownership: You will work closely with researchers and other engineers to understand requirements and constraints, translate fast-moving needs into reusable infrastructure, and own systems end-to-end, from design through operation.
What We Hope You'll Bring
- Extremely strong first-principles thinking.
- Familiarity with agent infrastructure, observability, security and sandboxing
- Deep experience with cloud platforms (GCP, AWS) and distributed systems: compute orchestration, networking, autoscaling, service meshes, load balancing.
- Ability to reason about system bottlenecks, performance tuning, and cost optimizations across compute, networking, and storage.
- Comfort with Kubernetes, cluster-level reliability, and service-oriented architectures.
- Solid intuition around scalability, performance, and failure modes.
- Experience with infrastructure-as-code (e.g. Terraform), containerization, and modern platform engineering practices.
- Familiarity with logging, metrics, tracing, incident response, SLOs, and debugging complex distributed systems.
- Strong cross-functional communication and ownership mindset.
- Experience (4-6 years) working in fast-moving or early-stage environments where ambiguity is normal with demonstrated growth trajectory.
Ultimately, we’re looking for someone who can spin up quickly on unfamiliar and ambiguous domains, has a strong sense of ownership, and cares deeply about our mission. Even if you don’t check all the boxes above, we strongly encourage you to apply if this sounds like you.
Bonus Points If You Have
- Experience with large-scale ML training, evaluation, or simulation infrastructure.
- Experience with secrets management systems (e.g., Doppler).
- Background in observability, cost optimization, or internal platform tooling.
- Exposure to robotics, simulation, or real-time systems.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Physical Intelligence, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
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.
Physical Intelligence AI Hiring
Physical Intelligence has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $261,000 across 2,646 tracked positions. That's 23% 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 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).
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 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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