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About This Role
AI\-Native Technical Product Manager — Ag Tech
US Midwest · Full\-time
Reports to: VP of Product
About The Position
Taranis is the world's leading AI\-powered crop intelligence platform, providing ag advisors and their growers with data\-driven agronomic recommendations that drive farm management and input decisions. Through full\-service, leaf\-level data capture, Taranis' insights and services allow growers to accelerate decision\-making, simplify management, maximize profitability, and enhance sustainable farming practices.
Since its founding, Taranis has worked with the world's top agricultural retailers and crop protection companies, serving millions of acres across the United States and globally. Taranis' Global Headquarters is located in Indianapolis, Indiana, with an R\&D and Innovation Center in Tel Aviv, Israel. To learn more, visit www.taranis.com.
The Role
We are seeking an AI\-Native Technical Product Manager to help translate the needs of our ag retail, agronomy, and grower customers into products and solutions our engineering team can build rapidly. This role sits close to both the customer and engineering: you'll spend real time with retailers and agronomists understanding how they deliver value to their growers and their business through the season, and collaborate with our AI\-native engineering team to turn that understanding into clear, well\-structured specs.
This is a hands\-on, technical product role. You'll be comfortable directing AI tools to draft and refine product specs, in alignment with guidelines and structures established by our engineering team. You'll partner closely with the engineering team to integrate sharp agronomic, business, and UX judgment into a requirement\-driven, AI\-native engineering process. This role is central to our strategy of empowering retailers to turn intelligence into action for their growers and their businesses through AI.
Location
Remote within the Midwest or based in Indianapolis, IN. This role requires regular travel to customer locations to work directly with their teams.
Requirements:
Required Qualifications
- Professional experience in agriculture or ag tech: retail, agronomy, grower\-facing, crop inputs, equipment, or another part of the industry
- Demonstrated hands\-on experience directing AI tools (e.g., Claude Code or similar) to draft, refine, and iterate on product specs, or to develop mock\-ups or clickable prototypes
- Strong, precise written communication: able to write requirements clear enough that there's exactly one way to interpret them
- Technical proficiency to lead credible conversations with engineers about tradeoffs and feasibility
- Aptitude for understanding internal customer systems (e.g., agronomy, CRM, ERP, inventory, dispatch) and translating them into requirements to support data integrations and workflows
- Self\-directed and comfortable owning outcomes with minimal oversight
- Willingness to travel regularly within the Midwest to work directly with customers
- Strong analytical, communication and collaboration skills
Preferred Qualifications (Advantage)
- Hands\-on experience (professional or personal) developing AI\-driven code, tools, or agents; deeply passionate about leveraging AI to accelerate productivity and support AI\-native product development
- Experience with precision agriculture, remote sensing, or crop data platforms
- Experience with agricultural equipment APIs, data layers and data integration
Responsibilities:
Customer \& Agronomic Discovery
- Spend regular time in the field and in the office with ag retail customers
- Understand how retail agronomists and sales staff drive grower value throughout the season—from pre\-season planning and in\-season decisions through to harvest—and how these efforts translate into business value for the retailer
- Identify where the friction is between insight and action, and where the greatest agronomic and economic value is going unrealized for growers
Spec\-Driven Product Development
- Direct AI tools to draft, refine, and iterate on product specs, requirements, and acceptance criteria with the precision required for direct engineering implementation
- Prototype against real customer and field data to validate hypotheses and test solutions
- Partner closely with engineering throughout the build, reviewing deliverables against original spec and intent, and tightening specs as needed
- Prioritize product opportunities within the product roadmap based on the agronomic and economic value they deliver to retailers and growers, acre by acre
Cross\-Functional Collaboration
- Co\-develop product specs in close partnership with engineering; you focus on business, user and UX requirements \- including consistency across the platform \- while engineering ensures alignment with technical and design standards for our platform, AI\-maintainability, architectural integrity, and implementation
- Partner closely with Commercial Sales, Customer Success and Marketing to ensure product direction reflects real field evidence and market needs
- Collaborate closely with Agronomy to ensure all technical solutions are grounded in sound agronomic science
- Communicate customer and grower needs clearly to engineering and leadership to inform roadmap decisions and help the broader team understand where Taranis can deliver the most value to retailers and growers
Why Join Taranis?:
At Taranis, we don't just innovate — we invest in our people.
- Empowered by Respect: We value diverse perspectives and ensure every voice is heard.
- Driven by Accountability: Ownership and transparency are foundational to how we work.
- United in Collaboration: Cross\-functional teamwork is how we win.
- Dedicated to Commitment: We support flexibility, balance, and long\-term growth.
- Inspired by Innovation: We challenge the status quo to advance agriculture globally.
Join Taranis and be part of a team committed to transforming agriculture through technology, sustainability, and measurable impact — for growers, retailers, and the future of farming.
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 TARANIS, 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.
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.
TARANIS AI Hiring
TARANIS has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Westfield, IN, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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