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About This Role
Join Pigment: The AI Platform Redefining Business Planning
Pigment is the AI\-powered business planning and performance management platform built for agility and scale. We connect people, data, and processes in one intuitive, feature\-rich solution, empowering every team—from Finance to HR—to build, adapt, and align strategic plans in real time.
Founded in 2019, Pigment is one of the fastest\-growing SaaS companies globally. Industry leaders like Unilever, Snowflake, Siemens, and DPD use Pigment daily to make more informed decisions and confidently navigate any scenario.
With a team of 600\+ across Paris, London, New York, Toronto, San Francisco and Austin, we've raised nearly $400M from top\-tier investors and were named a Visionary in the 2024 Gartner® Magic Quadrant™ for Financial Planning Software.
At Pigment, we take smart risks, celebrate bold ideas, and challenge the status quo—all while working as one team. If you're driven by innovation and ready to make an impact at scale, we’d love to hear from you.What you’ll do
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We are looking for an AI Deployment Strategist to join our growing team. In this role, you will work directly with customers to design, build, and deploy AI\-powered planning solutions using Pigment.
This is a highly cross\-functional role at the intersection of engineering, data, and business problem\-solving. You will own the end\-to\-end implementation of Pigment’s AI capabilities for strategic customers — from translating business problems into models, to deploying agentic AI solutions in production.
You will play a key role in shaping how organizations leverage Pigment to transform their planning processes, while acting as a critical feedback loop between customers and our Product and Engineering teams.
What you’ll work on
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### Build \& deploy AI\-powered solutions
- Translate complex business problems into scalable Pigment models using AI agents, formulas, dimensions, and advanced logic
- Design, build, and deploy custom AI agents tailored to customer workflows and decision\-making processes
- Own end\-to\-end implementation of Pigment for key customers
### Work directly with customers
- Partner with business and technical stakeholders to rethink processes and design AI\-driven solutions
- Act as a trusted advisor, guiding customers on best practices
- Drive adoption and ensure customers maximize value from Pigment
### Prototype \& innovate
- Develop prototypes and experimental AI use cases to solve emerging customer needs
- Test and iterate on new approaches to agent\-based planning and automation
### Bridge product, engineering, and customers
- Collaborate closely with Customer Success, Solutions Architects, and Product teams
- Translate customer feedback into clear product requirements and specifications
- Contribute to the evolution of Pigment’s AI capabilities
### Enable and scale
- Train users and teams on Pigment features, modeling best practices, and scalable system design
- Help define repeatable patterns and frameworks for AI deployments across customers
What we’re looking for
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### Required
- Engineering or computer science degree from top tier institution
- 2–5 years in a technical, client\-facing, or implementation role, such as:
+ Forward Deployed Engineer
+ Data Scientist / AI Engineer
+ Solutions Architect / Solutions Engineer
+ Technical or Implementation Consultant
- Strong analytical and problem\-solving skills with experience in:
+ Data modeling, analytics, or business logic design
+ AI / ML concepts or applied data workflows
- Proficiency with:
+ Formulas, logic, and structured modeling
+ Programming (Python, SQL, or similar is a plus)
- Familiarity with:
+ SaaS platforms or planning tools
+ APIs, data pipelines, and system integration concepts
- Ability to manage multiple stakeholders and projects in fast\-paced environments
### Nice to have
- Background in FP\&A, financial modeling, or business planning workflows
- Prior experience with Pigment or similar platforms
- Additional European language(s) outside of English
What success looks like
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- Customers successfully deploy and adopt AI\-powered planning workflows
- Innovative agentic AI solutions are built and scaled across use cases
- You are seen as a trusted technical partner by customers
- Strong feedback loops drive continuous product improvement
- Repeatable patterns emerge to scale AI deployments across Pigment customers
\#LI\-HYBRID
\#LI\-LS1
*We conduct background checks as part of our hiring process, in accordance with applicable laws and regulations in the countries where we operate. This may include verification of employment history, education, and, where legally permitted, criminal records. Any checks will be conducted lawfully prior to formal employment contracts being signed, with candidate consent, and information will be treated confidentially.*
*Pigment is an equal opportunity employer. We believe diversity is a strength and fosters innovation. We are committed to enabling everyone to feel included and valued at the workplace. All qualified applicants will receive consideration for employment without regard to age, color, family, gender identity, marital status, national origin, physical or mental disability, sex (including pregnancy), sexual orientation, social origin, or any other characteristic protected by applicable laws. We may process your personal data in accordance with our* *HR Data Protection Notice**.*
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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 Pigment, 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.
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.
Pigment AI Hiring
Pigment has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% 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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