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Why work at Higgsfield AI?
Higgsfield AI is the fastest\-scaling generative AI company in history, hitting $500M in annual revenue run rate, 25M\+ users worldwide, 6M\+ generations per day, and powering 390 of Fortune 500 brands.
We're building at the absolute frontier of AI\-powered video creation and next\-generation creative tools. Joining Higgsfield means becoming part of a high\-impact team shaping the future of AI\-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like.
Role Overview
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Higgsfield AI is seeking a Director of Partnerships, AI Compute to lead strategic partnerships across our global compute ecosystem.
This is a highly cross\-functional role focused on identifying partnership opportunities, developing commercial strategies, negotiating agreements, and serving as the primary relationship owner for key infrastructure partners. You'll work closely with leadership, Finance, Marketing, Legal, Engineering, Product, and external executives to secure partnerships that enable Higgsfield's continued hypergrowth.
The ideal candidate combines the structured problem\-solving of a management consultant with exceptional relationship management and commercial negotiation skills. You are comfortable navigating ambiguity, aligning diverse stakeholders, and driving complex initiatives from strategy through execution.
What You'll Do
Build Strategic Partnerships
- Identify, evaluate, and develop strategic partnerships across cloud infrastructure, GPU providers, hardware vendors, and other compute partners.
- Own relationships with key external partners from initial engagement through long\-term account management.
- Develop partnership strategies that align with Higgsfield's business objectives and infrastructure roadmap.
- Identify opportunities to expand strategic partnerships over time.
Lead Commercial Negotiations
- Structure and negotiate commercial agreements that maximize long\-term value.
- Coordinate negotiations across Legal, Finance, Marketing, Product, and Engineering stakeholders.
- Drive contract discussions through execution while balancing technical, operational, and commercial priorities.
- Manage ongoing commercial relationships and partnership performance.
Drive Cross\-Functional Alignment
- Serve as the central point of contact for strategic partnership initiatives.
- Identify key internal and external stakeholders and align priorities across multiple organizations.
- Understand partner motivations, business objectives, and decision\-making processes.
- Build consensus and remove blockers to accelerate partnership execution.
Influence Business Strategy
- Conduct market research and competitive analysis across the AI infrastructure ecosystem.
- Develop business cases and executive\-level recommendations for strategic partnerships.
- Monitor industry trends and identify emerging partnership opportunities.
- Support executive leadership with strategic planning and partnership reporting.
What We're Looking For
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- 5–10 years of experience in management consulting, strategy, business development, strategic partnerships, corporate development, investment banking, or related experience.
- Experience leading complex, cross\-functional strategic initiatives.
- Strong commercial negotiation and contract management experience.
- Exceptional executive presence and stakeholder management skills.
- Demonstrated ability to navigate complex organizations and influence without direct authority.
- Strong analytical and structured problem\-solving skills.
- Experience building business cases and presenting recommendations to executive leadership.
- Excellent written, verbal, and presentation skills.
- Ability to manage multiple high\-priority initiatives in a fast\-paced environment.
- Experience working with Legal, Finance, Marketing, Product, and Engineering teams.
- Experience within AI, cloud infrastructure, hyperscalers, semiconductors, GPU providers, enterprise technology, or adjacent industries.
- Experience negotiating strategic technology partnerships or infrastructure agreements.
- Background in management consulting (McKinsey, Bain, BCG, or similar), strategy, or corporate development.
- Familiarity with AI infrastructure, cloud computing, or large\-scale distributed systems.
- Experience working at a high\-growth, venture\-backed technology company.
Compensation \& Benefits
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We offer a competitive and thoughtfully structured compensation package designed to align with impact and growth.
Base Salary: The anticipated salary range for this role is $225,000– $275,000, depending on experience, skills, and location.
In addition to cash compensation, we offer equity and a comprehensive benefits package to support you both professionally and personally.
Higgsfield AI is an equal opportunity employer. We are committed to building a diverse and inclusive team and do not discriminate on the basis of race, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, disability, veteran status, or any other protected characteristic.
Compensation Range: $225K \- $275K
Salary Context
This $225K-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Higgsfield, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($250K) sits 14% above the category median. Disclosed range: $225K to $275K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Higgsfield AI Hiring
Higgsfield has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $275K - $275K.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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