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About This Role
Digital Solutions \& AI Manager
The Digital Solutions \& AI Manager role serves as a technical catalyst within Trust In Food, building and implementing AI\-powered tools and digital solutions that enhance our team's capabilities and our partners' experiences. This role bridges technology and agriculture, working closely with our Business Systems and IT teams at Farm Journal to deploy practical solutions that leverage our proprietary behavioral insights database and help producers make better decisions.
This is an ideal role for someone who is passionate about using technology to solve real\-world agricultural challenges and who can translate complex technical concepts into practical tools that non\-technical teams and clients can confidently use.
Trust In Food has the benefit of engaging directly with the nation’s top agri\-media brands, trusted for \~150 years in U.S. agriculture. This role will closely coordinate with our IT, marketing, and intelligence professionals as well as journalists, media anchors, podcasters, and other professionals to deliver what’s important and what’s next in sustainable agriculture.
Essential Duties and Responsibilities
*These duties include, but are not limited to, the following. Other duties may be assigned.*
- Design, implement, and optimize AI\-powered tools, digital solutions, and automation workflows that improve Trust In Food's operations, team productivity, partner and producer experiences.
- Identify opportunities to streamline business processes through technology, including enhancing workflows, automations, dashboards, reporting, and integrations within platforms such as Monday.com and HubSpot.
- Support CRM optimization initiatives, including the implementation and ongoing management of HubSpot to improve enrollment tracking, partnership management, pipeline visibility, reporting, and data quality.
- Develop and maintain digital tools, dashboards, and data visualizations that transform behavioral insights, program data, and sustainability metrics into actionable information for internal teams and partners.
- Support the design, implementation, and continuous improvement of Trust In Food partnerships and pilot programs by developing digital tools, AI solutions, automation, and reporting that enhance participant experiences and program outcomes.
- Leverage AI technologies to improve event planning, content development and other business processes across Trust In Food.
- Serve as the primary liaison between Trust In Food and Farm Journal Business Systems, IT, Intelligence, Marketing, and other cross\-functional teams to identify technology needs, prioritize solutions, and ensure successful implementation and adoption.
- Develop training materials, documentation, and user resources that promote adoption of AI tools, CRM systems, digital platforms, and workflow improvements by internal teams and external partners.
- Provide technical support, troubleshoot issues, and evaluate emerging technologies to ensure solutions remain scalable, user\-friendly, and aligned with Trust In Food's strategic goals.
Skills/Professional Experience
*These qualifications include, but are not limited to, the following:*
- Bachelor’s degree in computer science, data science, agricultural technology, information systems, or related field
- 6\+ years of experience in software development, digital solutions, data analytics, or related technical role, experience in agriculture / ag tech / farm management software / sustainability software preferred
- Demonstrated experience building practical applications or tools that non\-technical users successfully adopt
- Proficiency in programming languages (Python, JavaScript, R, or similar)
- Experience with AI/ML tools and platforms (OpenAI API, cloud AI services, or similar)
- Strong skills in data visualization and dashboard development (Tableau, Power BI, Excel, or similar)
- Database knowledge (SQL, data modeling)
- Experience with web development frameworks and low\-code/no\-code platforms
- Familiarity with API integration and automation tools, particularly in sustainability and farm management systems
- Excellent ability to translate technical concepts for non\-technical audiences
- Strong training and presentation skills
- User\-focused mindset—builds for actual user needs, not just technical elegance
- Self\-directed and able to manage multiple projects simultaneously
- Collaborative approach to working with cross\-functional teams
Work authorization requirementsMust have unrestricted work authorization to work in the United States.
EEO statementFarm Journal is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran, or disability status.
Other dutiesPlease note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the team member for this job. Duties, responsibilities, and activities may change
Salary Context
This $80K-$85K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Farm Journal, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($82K) sits 62% below the category median. Disclosed range: $80K to $85K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Farm Journal AI Hiring
Farm Journal has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $85K - $85K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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