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About This Role
Description
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About the Role
The Product Forward Deployed Engineering (FDE) team is Salesforce's zero\-to\-one engine for new agentic product innovation. We embed directly with our most strategic customers to validate emerging AI capabilities, harden the platform under real\-world conditions, and transform every field insight into repeatable patterns that shape the product roadmap.
We are looking for a Senior Director, Forward Deployed Engineering to lead our Agentforce Supply Chain organization.
This is a senior technical leadership role at the intersection of AI, enterprise supply chain, and product development. You'll embed expert engineering talent with strategic customers to own and evolve the agentic workflow infrastructure powering Agentforce Supply Chain — purpose\-built for complex, multi\-party manufacturing environments. In doing so, you'll incubate emerging capabilities, validate pre\-GA and early GA features, and generate the product signal that drives roadmap priorities — ensuring what we learn in the field directly shapes what gets built next.
Your Impact
### Leadership \& Team Management
- Lead a High\-Performing Team: Recruit, mentor, and develop a global team of Product FDEs, driving technical excellence and career growth at scale. Own organizational design, headcount planning, and capacity strategy to ensure the team is resourced to deliver impact across the field.
- Ensure Operational Excellence: Define and monitor key performance indicators — including field intelligence reports, technical gap analyses, product opportunity assessments, and internal AI adoption. Coach your team on improvement areas and effectively manage complex engagements.
- Diversity \& Equality: You'll create a diverse, inclusive and psychologically safe environment for your team.
### Technical Strategy \& Execution
- Maintain Technical Authority: Serve as the executive\-level technical authority for Agentforce Supply Chain. Stay current on platform capabilities and partner with Product \& Engineering to ensure the FDE organization has deep expertise in existing and upcoming releases.
- Scale Frameworks \& Assets: Champion the development of standardized engagement methodologies, technical playbooks, and reusable assets — including agents, automation scripts, and diagnostic frameworks — that can be scaled across the full FDE organization.
- Guide Complex Engagements: Lead your team in assessing R\&D opportunities, defining research hypotheses, and identifying opportunities to create new product capabilities and accelerators that strengthen the global platform and apps. Engage directly on the highest\-complexity technical challenges where executive presence and deep expertise are required.
- Influence Product: Act as the voice of the field with Product and Engineering. Translate patterns from customer deployments into actionable product feedback, shaping roadmap priorities and tooling investments that accelerate platform adoption at scale.
### Customer \& Business Impact
- Drive Success: Build and sustain executive\-level relationships across a strategic customer portfolio. Ensure customers realize measurable business value from Agentforce deployments, driving adoption depth and long\-term consumption growth.
- Manage Portfolio Performance: Operationally manage your portfolio, monitoring key metrics including migration timelines, technical debt reduction, and post\-migration health scores to accelerate progress toward completion goals.
- Strategic Alignment: Partner closely with Sales, Customer Success, Product, and Professional Services teams to ensure customers benefit from R\&D engagement while capturing lessons learned and feeding insights back into the platform and apps.
- Thought Leadership: Maintain a sharp point of view on the competitive landscape — continuously identifying and validating Salesforce's key differentiators. Translate field intelligence into thought leadership, best practices, and enablement content.
Required Qualifications
- Proven Leadership: 5\+ years in direct people management or indirect leadership roles, with a track record in talent management and technical team building.
- Industry Expertise: Practical experience in Supply Chain Management (SCM), Enterprise Resource Planning (ERP), Manufacturing Execution Systems (MES), or similar complex enterprise solutions.
- AI/Agentic Platform Authority: Demonstrated track record deploying AI/LLM\-based solutions at scale — including agentic frameworks, prompt engineering, and retrieval\-augmented generation (RAG) — with the credibility to represent Salesforce's AI vision at the executive level with customers and partners.
- Technical Fluency: Working proficiency in one or more programming languages (Python, JavaScript, Apex, or Java); able to engage credibly with engineers and architects without relying on intermediaries.
- Strategic Problem\-Solving: Proven ability to diagnose systemic failure patterns — whether product, configuration, or data — and translate ambiguous field signals into structured, actionable insights for Product \& Engineering.
- Executive Communication: Exceptional written and verbal communication skills; able to distill complex technical findings into crisp narratives that resonate with C\-suite customers and internal leadership without requiring follow\-up.
- Operational Intelligence Mindset: Experience leveraging product telemetry, observability data, and field intelligence to identify platform patterns, surface product gaps, and inform R\&D prioritization at an organizational level.
- High Agency \& Organizational Agility: Track record of building and leading teams that thrive in ambiguous, high\-stakes environments — instilling a culture of experimentation, fast iteration, and continuous learning across the organization.
- Willingness to Travel: Ability to travel 20–30% to customer sites and company engagements.
Preferred Qualifications
- Prior leadership experience in a forward deployed engineering, solutions engineering, or technical advisory organization — with direct accountability for field outcomes and customer technical success.
- Degree in Industrial Engineering, Supply Chain Management, Computer Science, or a related technical field (or equivalent relevant experience).
- MBA or an advanced degree in Industrial Engineering, Supply Chain Management, or Operations Research.
- Hands\-on experience with supply chain workflow platforms (e.g., Regrello, Coupa, o9 Solutions, Blue Yonder, or similar).
- Experience designing target operating models for global supply chains with complex multi\-site, multi\-party requirements.
- Experience with Salesforce CRM, Supply Chain Management, or comparable enterprise systems.
- Hands\-on familiarity with Salesforce CRM, Agentforce platform, and Data 360; fluency with adjacent enterprise platforms a strong plus.
- Proven ability to build thought leadership presence — through content, community engagement, or executive influence — that elevates both team credibility and platform adoption.
For roles in San Francisco and Los Angeles: Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Informatica, 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 $166,983 based on 13,781 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,288.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Informatica AI Hiring
Informatica has 39 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, AI Architect, AI Software Engineer. Positions span IN, US, CA, US, TX, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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