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About This Role
Simplot is a family\-owned, privately held global food and agriculture company headquartered in Boise, Idaho. Its integrated portfolio of companies includes food processing and food brands, phosphate mining, fertilizer manufacturing, farming, ranching and cattle production, and other enterprises related to agriculture. Simplot has major operations in nine countries with products and services available to customers worldwide.
Summary
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This valued team member would design and develop technical solutions for multiple lines of business, regionally and internationally, using new and exciting technologies. In this role, you would design, code, test and implement complex AI solutions leveraging emerging agentic architectures, with the freedom to challenge the status quo. An ideal candidate is comfortable providing technical expertise, leadership, and guidance to help drive the future of technology at Simplot.
We are looking for an individual who:
- Values collaboration with like\-minded teammates
- Enjoys tackling challenging, complex problems using both new and existing technologies
- Exudes a desire to continuously learn, grow, contribute to a learning culture, and help educate all members of the team
- Possesses an agile mindset
- Appreciates the stability of a global family\-owned enterprise
- Aspires to be part of something bigger than themselves and to contribute to feeding the world
- Seeks a positive work/life balance
- Has a passion for people
Key Responsibilities
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- Design, develop, and implement AI\-enabled solutions that solve practical business problems across Simplot’s global lines of business
- Build and integrate generative AI applications, including large language model solutions, retrieval\-augmented generation patterns, intelligent agents, and automation workflows
- Collaborate with data scientists, data engineers, product owners, business stakeholders, and vendors to translate business needs into scalable, secure, and maintainable AI solutions
- Evaluate, test, and monitor AI systems for accuracy, reliability, performance, safety, and business value, using clear success metrics and continuous feedback loops
- Deploy and support AI solutions in production environments using modern engineering practices, including Iac, CI/CD, observability, version control, and cloud\-native architecture
- Apply responsible AI practices, including data privacy, security, transparency, human oversight, and appropriate guardrails for enterprise use cases
- Share knowledge, patterns, and reusable components with supporting teams to grow AI engineering capability and accelerate adoption across the organization
- Contribute to the collective knowledge of the team by capturing patterns, decisions, infrastructure, prompts, evaluations, and reusable practices in version\-controlled, discoverable assets
Relevant Experience
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- Bachelor’s degree in CS or a relevant engineering discipline preferred
- Strong experience with Git
- Strong understanding of object\-oriented principles. We primarily leverage C\# and Python for back\-end development, but are open to candidates having object\-oriented expertise in other languages such as Go, Java, TypeScript, etc.
- Strong understanding of common software design patterns and software development best practices
- Experience with Azure, Open ID Connect, and Entra ID a plus
- Understanding of cloud\-native development in a hybrid enterprise ecosystem, including public cloud, on\-premises platforms, containerized workloads, DevOps practices, CI/CD, and infrastructure as code is a plus
- Experience developing agentic AI solutions and understanding emerging agent interoperability and context protocols such as MCP, A2A, and ACP
- Hands\-on experience designing and delivering LLM\-powered solutions using modern AI solution architecture patterns, including prompt engineering, retrieval\-augmented generation, orchestration frameworks, evaluation strategies, and secure enterprise integration
Job Requisition ID: 26614
Travel Required: Less than 10%
Location(s): Simplot Headquarters \- Boise
Country: United States
\*\*The Simplot Company is proud to be an Equal Opportunity Employer and will consider all qualified applicants for employment without regard to race, color, religion, national origin, ancestry, age, sex, gender, gender identity, gender expression, genetic information, physical or mental disability, medical condition, sexual orientation, military or veteran status, marital status, or any other protected status.
If the Simplot Company decides to offer you this position, such offer will be conditioned on your satisfactory completion of a post\-offer criminal background check. For Washington Job Applicants, see the Washington State Attorney General’s Washington Fair Chance Act Guide and RCW 49\.94\.010 for more information.\*\*
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 Simplot Company, 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.
Simplot Company AI Hiring
Simplot Company has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boise, ID, 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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