Interested in this AI/ML Engineer role at Traction Ag?
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About This Role
Traction Ag helps farmers simplify the business of farming through cloud\-based software that brings together farm financials and operations. We’re hiring a Director of Marketing to lead and scale a modern B2B SaaS marketing engine focused on pipeline growth, brand positioning, and revenue impact.
In this role, you will operate as a cross\-functional technical leader partnering closely with the COO and engineering leadership. You will help define the company’s AI architecture, tooling standards, and governance practices.
Core Priorities
- Build a secure internal AI data and retrieval layer
- Establish governance and safe AI usage patterns
- Ship high\-leverage internal workflows and automations
- Enable responsible AI adoption across the company
- Create scalable foundations for future agentic systems
What the role is not:
- An AI research role
- A pure ML modeling role
- A prompt engineering role
- A people management role
- A speculative innovation lab
What You Will Build
The Operating Layer
Our internal AI operating layer. A secure internal AI layer that connects company knowledge systems and makes institutional context searchable, usable, and operational.
- Building AI\-powered retrieval and synthesis workflows across Slack, CRM, Google, docs, project management, and meeting transcripts so teams can access institutional knowledge and historical context in seconds
- Creating scalable systems for meeting capture, decision logging, onboarding, SOP generation, and cross\-functional communication
- Implementing RAG pipelines, vector search, embeddings, and AI orchestration frameworks that power the entire internal AI toolkit
- Reducing knowledge silos, duplicated work, and dependency on tribal knowledge by making information flow to where it is needed, when it is needed
The Internal AI Workflow Platform
A centralized library of reusable AI\-powered workflows, automations, and internal tools employees can safely use without exposing sensitive company or customer data.
- Curated, tested AI workflows for each department that non\-technical team members can invoke without prompt engineering from scratch
- Version control, access governance, and audit trails so the organization can scale AI usage without sacrificing security or consistency
- A framework that lets team members go from idea to prototype to production\-ready workflow, with guardrails that keep outputs safe and on\-brand
Operational Intelligence
- Automations and agents that transform raw information into actionable insights, summaries, tasks, and operational reporting
- Tools that make operational metrics, goal tracking, and leadership reporting more accessible, more actionable, and harder to ignore
- Governance, security, and data quality standards for every internal AI system
Security \& Governance
- Define safe AI usage standards across the organization
- Establish data handling and model access policies aligned with security requirements
- Evaluate AI vendors, infrastructure, and deployment patterns for security and scalability
- Design human\-in\-the\-loop workflows, auditability, and operational safeguards
- Ensure customer financial data is protected across all AI systems
What We Are Looking For
Required
- 7\+ years in software engineering, data engineering, or platform/infrastructure roles, with at least 2 years focused on AI/ML systems or AI\-powered tooling
- Demonstrated track record designing and implementing AI\-powered retrieval systems, knowledge architectures, and workflow orchestration patterns in production environments.
- Proficiency in Python, Node, Angular, and TypeScript; comfortable working across the stack from data pipelines to lightweight front\-end interfaces
- Proven ability to build integrations across SaaS tools using APIs, webhooks, and automation platforms
- Strong understanding of context engineering: designing retrieval strategies, memory systems, and information architectures that make AI outputs reliable and high\-quality
- Excellent communication: you can translate between technical architecture and business outcomes, and you can teach complex concepts to non\-technical colleagues
- Comfortable operating autonomously, prioritizing ambiguous problems, and making pragmatic technical tradeoffs.
Nice to Have
- Familiarity with structured operating systems for scaling companies
- Background in ag\-tech, fintech, or B2B SaaS
- Experience building internal developer platforms, plugin systems, or self\-service tooling for non\-engineers
- Contributions to open\-source AI tooling or a portfolio of internal tools you have built and shipped
- Experience with our stack: Atlassian, Notion (including the API), HubSpot, Slack, Jira, Figma, Google Workspace, Canva
What Success Looks Like
Foundation
- Initial secure AI retrieval architecture is operational against at least one core company data source
- Foundational AI infrastructure, governance standards, and approved tooling patterns are established
- At least two vetted internal AI workflows are published and actively used
Quick Wins \- First 90 Days
- Three to five automations are shipped and saving measurable time across multiple departments
- At least one cross\-functional AI workflow is operational and adopted by non\-technical teams
- A prioritized six\-month roadmap for AI infrastructure, workflow automation, and governance is delivered to leadership
Organizational Trust
- You have established strong working relationships across department leadership
- The organization trusts the systems, guardrails, and architectural direction being established
- The company has begun moving from fragmented AI experimentation toward secure, production\-oriented AI adoption
What We Offer
- Mission\-driven work that directly supports farmers and rural communities.
- A nimble, passionate team where your ideas have real impact.
- Competitive and cost\-effective benefits plans \- Health, Dental, Vision, and Life Insurance
- 401(k) Plans with Company Match
- Unlimited Paid Time Off
- Paid Holidays
- A company culture rooted in our values:
+ Put the Farmer First
+ Gain Traction as a Team
+ Think Outside the Silo
+ Take the Right Next Step
+ Choose Joy
Salary Context
This $170K-$190K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Traction Ag, 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. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $170K to $190K.
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.
Traction Ag AI Hiring
Traction Ag has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $190K - $190K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 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 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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