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About This Role
Who We Are
At Partnerize, we're on a mission to transform the way businesses grow. We've built the leading partnership automation platform that empowers brands to discover, engage, and convert their audiences at scale. From affiliate marketing to influencer collaborations, we help our clients build and manage profitable partnerships that drive real results. We're a team of passionate problem-solvers who are dedicated to helping our clients win in the ever-evolving world of digital marketing.
Why Join Us
We're looking for passionate, talented people who want to be part of a winning team. At Partnerize, you'll find a culture of collaboration, innovation, and respect. We're guided by our core values, and we're committed to creating an environment where everyone can do their best work. We also offer a competitive salary, generous benefits, and a flexible work environment that allows you to thrive both personally and professionally. If you're ready to grow your career and make a difference, we'd love to hear from you.
About the Role
Most companies are still talking about AI. We want someone who will put it to work.
As Director, Business Systems & AI Enablement, you'll lead how Partnerize uses technology to run a smarter, faster business. You'll own Salesforce and Asana, the core platforms our teams use every day, along with the integrations that connect them to other business systems such as NetSuite, HubSpot and DocuSign. You'll also bring enterprise AI tools like Google Gemini Enterprise into how we work, so our teams spend less time on manual work and more time on customers and growth.
You'll be a player-coach. You'll lead and develop a small team of two while rolling up your sleeves yourself. One week you might be redesigning a Salesforce sales workflow. The next, you might be building a Gemini agent that removes hours of admin from the Sales team, or presenting ROI results to the executive team.
What you'll own:
- Business systems: the roadmap, intake, delivery and vendor relationships for Salesforce, Asana and the integrations that connect them across the business.
- Process optimization: finding friction, redesigning workflows and making sure data stays clean as we scale.
- AI enablement: rolling out enterprise AI, designing functional agents, driving adoption and measuring the results.
You won't be doing this alone. You'll be part of a leadership group shaping how Partnerize moves into a human-AI enabled future, with one shared goal: a workplace where people and AI do their best work together. Our Director, Product brings the vision for AI in the Partnerize platform. Our Director, Business Performance Optimization brings the framework we use to evaluate new AI opportunities and helps guide proofs of concept. Our People Success team makes sure our people grow alongside the technology. You bring it all to life across the business: working with teams to find where AI can make the biggest difference, then turning big ideas into real change in how we work every day.This role has high visibility and real autonomy. You'll have a direct line to measurable business impact.
Key Responsibilities
1. Business Systems & Process Optimization
- Salesforce & Asana ownership: Own the configuration, roadmap and health of Salesforce and Asana as our primary business platforms, ensuring they scale with the business and support clean, reliable data.
- Integration oversight: Oversee the integrations between Salesforce and Asana and connected systems, including NetSuite, HubSpot and DocuSign, so data flows accurately across Sales, Marketing, Finance and Operations.
- Process optimization: Find friction in how teams work and redesign processes to improve efficiency, data quality and scalability.
- Requirements and solution design: Partner with stakeholders across Sales, Marketing, Finance and Operations to develop user stories, define solution designs and write clear business requirements.
- Agile delivery: Manage intake and triage for business systems requests. Prioritize and maintain backlogs in Jira so developers can deliver smoothly.
- Hands-on building: Personally configure, prototype and build solutions (e.g., Salesforce Flows, Asana workflows, Gemini agents) alongside your team.
- Testing and quality: Own the strategy and execution of user acceptance testing (UAT) so delivered solutions meet business requirements.
- Change management and enablement: Lead end-user training, documentation, process flows, data dictionaries and communications for new features and workflow changes.
2. AI Enablement & Agent Adoption
This role leads internal AI adoption in close partnership with Product, Business Performance Optimization and People Success.
- Enterprise AI platform ownership: Act as the internal product owner for enterprise AI platforms, starting with Google Gemini Enterprise. Manage rollouts, end-user enablement, prompt templates and feature adoption.
- Agent and workflow design: Lead the design, testing and deployment of functional, business-led agents and automated multi-step workflows that remove manual work across teams.
- AI embedded in core systems: Identify opportunities to embed AI and automation directly into Salesforce and Asana workflows, not just alongside them.
- AI opportunity delivery: Work with business teams to find high-impact AI use cases. Take them through the Business Performance Optimization evaluation framework, partner on proofs of concept, and lead approved tools from pilot to full rollout.
- AI governance: Put responsible-use guidelines, data security practices and access controls into practice for internal AI tools, in partnership with IT, Security and Legal.
- Impact measurement: Define, track and report on business outcomes such as hours saved, cycle-time reduction and adoption rates, with seat utilization as one input, to show the ROI of AI across departments.
3. Leadership & Strategy
- Team leadership: Lead, coach and develop a team of two business systems professionals, and manage external consultants as needed. Set clear goals, run performance reviews and support ongoing growth.
- Roadmap and prioritization: Own the consolidated Business Systems and AI roadmap across departments. Balance competing requests against company priorities, and explain "No" or "Not Yet" decisions clearly.
- Vendor and license management: Manage relationships, SLAs and engagements with software vendors and implementation partners. Oversee renewals and license optimization to control cost.
- Key partnerships: Work closely with the Director, Product, sharing AI learnings, tools and vendor insights so internal and product AI efforts stay aligned. Partner with the Director, Business Performance Optimization to take AI opportunities through the evaluation framework and proofs of concept to delivered, measurable business results. Work with People Success on AI skills, change readiness and how roles evolve as AI takes on more routine work.
- Internal champion: Advocate for technology and AI modernization across the company, and communicate the value the Business Systems team delivers to executives and department heads.
What Success Looks Like
- First 90 days: Assess the current Salesforce, Asana and integration landscape. Build relationships with key stakeholders. Deliver an initial prioritized Business Systems and AI roadmap.
- First 6 months: Launch at least [X] functional AI agents or automated workflows in production. Establish an AI adoption and impact dashboard. Streamline intake and delivery processes.
- First 12 months: Reach 100% active adoption of Gemini Enterprise. Show measurable time savings of [X] hours per month across teams. Present a roadmap approved by the executive team for the following year.
What You Bring
Experience
- 7+ years of progressive experience in business systems, business process optimization or enterprise application management.
- Experience leading, coaching or managing business analysts, systems administrators or similar roles. You enjoy building as much as leading.
- Bachelor's degree in Information Systems, Computer Science, Business Administration or a related field, or equivalent practical experience.
Technical Skills
- Salesforce: Deep hands-on administrator or power-user experience, including automation (e.g., Flows) and data management.
- Asana or similar work management platforms: Experience configuring and scaling tools such as Asana, Monday.com, Smartsheet or similar.
- Integrations: Working knowledge of how core platforms connect to systems like NetSuite, HubSpot and DocuSign. A solid understanding of APIs, data models, ETL and data sync between systems.
- AI and automation: Practical experience rolling out and driving adoption of commercial AI tools (e.g., Google Gemini Enterprise, ChatGPT Enterprise, Microsoft Copilot), including prompt design, agent or workflow building and measuring impact. Experience with automation platforms (e.g., Zapier, Workato) is a plus.
- Agile delivery: Experience managing backlogs, prioritizing sprints and writing user stories in Jira.
Leadership & Communication
- Excellent written and verbal communication skills, with the ability to explain technical workflows in clear business language.
- A collaborative style, with experience delivering results through peer partnerships where ownership is shared across functions.
- Proven ability to manage executive expectations, work through competing requests and apply prioritization frameworks with tact.
- Comfortable in a fast-paced environment with changing priorities and technologies.
Nice-to-Haves
- Salesforce certification(s) (e.g., Administrator, Advanced Administrator, Business Analyst).
- Experience with Salesforce AI capabilities
- Familiarity with digital, performance, affiliate or partner marketing.
- Experience supporting users across multiple regions (EMEA, Americas, APAC).
- Training or certification in process improvement methods (e.g., Lean, Six Sigma).
US Benefits & Perks
- Flexible Time Off Policy – Take the time you need
- Enhanced Parental Leave: 16 weeks for birth parent, 8 weeks for non-birth parent at full pay after one year employment
- Robust Company Sponsored Health & Welfare Benefits
- 401k Plan – 4% employee match
- Enhanced Wellness Program including access to EAP and Wellness Coaching
- Regular company events and activities
Our Commitment to Diversity & Inclusion
We are committed to attracting, developing, and advancing our outstanding team members, regardless of race, ethnic identity, sexual orientation, religion, age, gender, gender identity, physical abilities, or any other dimension of diversity. We strive to foster an environment where people can be their authentic selves, raise concerns and innovate, all without fear; where they are treated fairly and respectfully, have equal access to opportunities and resources and can contribute fully to the organization's success. Every individual in our business is expected to live this commitment without exception.
Privacy and data protection. The data collected as part of this application will be used for the recruitment process and any subsequent employment. You can find further information in Partnerize privacy policy here: https://partnerize.com/privacy-policy/
Notice to Recruiters and Staffing Agencies: To protect the interests of all parties, Partnerize will not accept unsolicited resumes from any source other than directly from a candidate or an approved vendor that has a written and signed agreement in place with Partnerize. Please do not contact or forward resumes to our company employees or locations. Any unsolicited resumes will be considered Partnerize property. Partnerize is not responsible for any charges or fees related to unsolicited resumes.
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 Partnerize, 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. Director-level AI roles across all categories have a median of $272,300.
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.
Partnerize AI Hiring
Partnerize has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Plymouth, MA, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 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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