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About This Role
Who are we?
Founded in 2005, Pierce Washington helps clients transform their quote\-to\-cash process. We are creating a next\-generation Total Commerce Company and will own the Total Commerce category – a fast\-growing segment in the tech industry. This is the place to come to if you want to work with the best, get in at the ground floor and help sustain and grow one of the best places to work. We have completed over 100 CPQ projects enabling leading enterprise companies to achieve their most complex Q2C process automation goals and transform how they do business.
Who are you?
Our Data Cloud \& AI Solutions Engineers are responsible for leading the technology evaluation stage for data\-driven and AI\-powered Salesforce solutions, primarily working with Salesforce’s Data Cloud and Agentforce. This role is a key technical advisor, collaborating with the sales team on customer\-focused meetings, demonstrations, and proofs of concept, emphasizing the transformative potential of data architectures and AI applications. You will be hands\-on in designing tailored data solutions, creating new use cases for Agentforce, and working with clients to craft data architectures that meet their specific requirements. You will work with C\-level executives to developers, where you will hone your ability to understand a customer’s needs, how it enables their business initiatives and deliver back an innovative solution that targets those needs. Must be able to identify all technical issues of assigned accounts to assure complete customer satisfaction through all stages of the sales process. Must also be able to establish and maintain strong relationships throughout the sales cycle.
What you get to do:* Develop and deliver engaging demonstrations of Salesforce Data Cloud, Agentforce, and other AI tools, integrated into other enterprise applications, illustrating their capabilities in driving business outcomes.
- Design and prototype cutting edge AI\-powered use cases, demonstrating innovative applications to improve customer engagement and service efficiency.
- Provide strategic pre\-sales technical guidance on data architectures, addressing client needs for data ingestion, integration, \& real\-time analytics.
- Serve as a trusted technical advisor to customers and partners, building strong relationships with their technical counterparts and stakeholders.
- Conduct proof of concepts and technical workshops, showcasing the potential of AI and data\-driven solutions to transform client operations.
- Collaborate with the sales team and marketing team to develop targeted packages and strategies that leverage Data Cloud and AI solutions to drive revenue.
What you bring to the role:* Previous Sales Engineering experience.
- Proven experience in architecting and delivering data\-centric and AI\-driven technical presentations, workshops, customized demos, and proof of concepts on Salesforce platforms.
- Deep understanding of data management, data architecture design, and the practical application of AI within Salesforce Data Cloud and Agentforce, or similar tools, and general knowledge of common AI applications and use cases.
- Strong communication and relationship\-building skills with a customer\-focused approach, capable of articulating how technical capabilities map to client needs.
- Ability to work autonomously and collaboratively with cross\-functional teams, including sales, engineering, and client stakeholders.
What we’ll do for you:* Competitive compensation package
- Health, vision, dental, life \& disability insurance
- Flexible, collaborative work environment with a commitment to work life balance
- Work anywhere, with full remote and hybrid options depending on location
- Learn and challenge yourself in a fast\-paced, growing tech company
More about us:
Since 2005, Pierce Washington has helped enterprises transform their quote\-to\-cash process in four ways: we implement CPQ, eCommerce and Billing \& Subscription Management solutions and we integrate those solutions to ERP and other enterprise systems. By focusing on these four areas – and doing them well – we have built our reputation as the go\-to partner for our clients. Our commitment to each client’s success is the foundation on which Pierce Washington was built. None of this success would have been possible without the synergy we have with our employees. Our commitment to each employee’s personal growth and development yet still highly valuing work/life balance, all within a culture of collaboration and teamwork, is why being a part of our team is an exciting next step in your career!
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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 Pierce Washington, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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.
Pierce Washington AI Hiring
Pierce Washington has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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