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About This Role
About Flexion At Flexion, we are building the autonomy stack for humanoid robots. Our mission is to drive the transition from fragile prototypes to real-world deployments of humanoids. We were founded by leading scientists in robot reinforcement learning (ex-Nvidia, ex-ETH Zürich) and backed by leading international VC firms. In just months, we went from our first line of code to deploying real humanoid capabilities with our customers, leveraging simulation and reinforcement learning. Today, we are rapidly expanding the capabilities of our autonomy stack, our customer base, and our team.
The role We are looking for an experienced ML engineer to join Flexion’s experienced infrastructure team and take ownership of Flexion’s GPU compute platforms. This is a senior, on-site role with significant scope.
At Flexion, we are building the brain for humanoid robots, which involves training foundation models with vast amounts of data on large GPU clusters. You will own the design, bring-up, operation and optimization of performant clusters. You will work with AI engineers to help them optimize their training speed and hardware utilization. You will also influence strategic compute planning and contribute to new tools and platforms for iterating on our AI models efficiently. This will put you at the heart of Flexion’s AI development and allow you to directly impact the execution of our ambitious roadmap. You will closely collaborate with the company’s leadership, engineers of the infrastructure team and AI engineers across the company.
Key responsibilities* Architect, run and continuously improve existing and future cloud-based GPU clusters. Select the best frameworks and tooling to run our clusters efficiently. Work on cluster provisioning, job schedulers and monitoring systems.
- Help AI engineers optimize their training workloads and maximize hardware utilization using profilers, contributing to our core ML libraries.
- Contribute to short- and long-term GPU compute strategies in collaboration with our AI engineering teams and help execute on them.
- Optimize capacity and cost by exploring multi-cloud strategies and evaluating trade-offs.
- Raise the bar on engineering practices, including testing, code quality, documentation, and system reliability.
Requirements
- Degree in Computer Science, Electrical Engineering or Software Engineering (or equivalent practical experience) plus significant industry experience.
- Hands-on experience with the training or inference of large models (billions of parameters) on distributed multi-node GPU hardware. This can include bringing up and running the cluster, writing and optimizing training/inference code, building ML pipelines, etc.
- Proficiency in Python and working knowledge of PyTorch.
- Deep understanding of distributed training concepts (DDP, FSDP, NCCL).
- Experience with at least one cloud platform (AWS, GCP, Azure or neoclouds) or large-scale on-premises GPU infrastructure.
- Experience with job scheduling and orchestration tools: Slurm and/or Kubernetes/KubeRay.
Nice-to-haves
- Familiarity with profilers (e.g., PyTorch Profiler, Dynolog, HTA, Nsight).
- Experience with high-performance or parallel file systems (e.g., Lustre).
- Experience provisioning compute on multiple cloud providers.
- Experience with infrastructure-as-code and configuration management (Terraform, Ansible).
Benefits
- Competitive Compensation
- Joining a leading robotics team & exposure to never-done-before research
- Energetic, collaborative culture with a bias for action and regular community events
Zurich
- Enhanced pension plan
- Relocation & permit sponsorship
- Enhanced holiday & paid leave perks
- Central Zürich office with top-tier robotics testing facilities and infrastructure
San Franciso
- 401(k) with company contributions
- Health, dental & vision coverage with the flexibility to choose your own plan
- Open PTO policy & paid company holidays
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 Flexion Robotics, 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.
Flexion Robotics AI Hiring
Flexion Robotics has 3 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, Research 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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