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About This Role
AI Solutions Engineer
The Good Company — Springfield, MO · \[Onsite / Hybrid — TBD] · Full\-time · \[$50k–$80k] \+ benefits
The short version
We're a fast\-growing 3PL that ships for e\-commerce brands people love — and we're rebuilding our data and tooling backbone from the ground up. We're hiring an engineer to help build it: someone who can design a data model, wire up an API, ship a tool, and stand behind how it works.
If your favorite thing in the world is looking at a slow, manual, duct\-taped process and thinking *"I could build something better by Friday"* — keep reading.
Who we are
Good Company does good work for good people. We're a Springfield, Missouri third\-party logistics company that our e\-commerce clients treat as an extension of their own brand. We just moved into a new facility, our volume roughly doubles every year, and our leadership team has made a real bet: that great tooling and AI, in the hands of people who care, is how a 3PL wins the next five years.
That bet is this role.
What you'll actually do
You'll join a small Information team in the middle of its most exciting chapter — replacing spreadsheets, legacy BI, and vendor workarounds with a modern platform we own:
- Build internal tools people use the next day — dashboards, ops tools, and client\-facing reporting on our stack (TypeScript, Next.js, Supabase/Postgres, BigQuery, Vercel).
- Connect systems that don't talk to each other — our WMS, billing, accounting, carrier, and Slack ecosystems all have APIs; you'll make them one system instead of ten.
- Solve problems worth real dollars — automated reporting, billing\-error detection, shipping\-anomaly monitoring. The problems here aren't hypothetical; catching them is worth six figures a year.
- Turn hours into software — every recurring manual process in the business is a candidate for automation, and you'll have the mandate to go after them.
- Own things end to end — you scope it, build it, ship it, and stand behind it. No ticket queue, no committee.
Who you are
We hire for traits first, résumé second:
- A problem\-solver who happens to use technology — you're drawn to the itch of an unsolved problem more than to any particular tool.
- Strong on fundamentals — you understand what's happening under the tools you use: how the data is modeled, what the API actually returns, why the query is slow, what breaks when the request fails.
- Fluent with AI, and accountable for it — you use AI to build and think faster, but you can read, debug, and explain every line it produces. If you can't say how it works, you don't ship it.
- A finisher — you ship. You'd rather deliver something real this week than something perfect never.
- Curious — you want to know *why* the number is wrong, not just that it is. (Ask us about our core values — curiosity is one of them, and we mean it.)
- An owner — when something you built breaks, you're the first to know and the first to fix it.
- A translator — you can sit with an account manager or a CFO, understand what they actually need, and come back with something better than what they asked for.
Nice to have (not required)
- SQL and database design (Postgres, BigQuery, or similar)
- API integration experience — REST, webhooks, OAuth, the fun parts and the cursed parts
- TypeScript, Python, or both
- Experience in logistics, e\-commerce, or another operations\-heavy business
What you get
- \[$50k–$80k] salary \+ \[benefits summary — health, PTO, 401k, etc.]
- A greenfield platform build with direct, visible P\&L impact
- No layers, no six\-week sprint ceremonies — you'll talk to the people who use what you build, often the same day you build it
- Leadership that has already bought into modern tooling — you're not here to convince anyone it matters
- A small team where your work is the difference, and everyone knows it
How to apply
Skip the cover letter. Send us:
- Your résumé (or LinkedIn, or GitHub — whatever tells your story best), and
- A short note about something you built that solved a real problem. What was broken, what you built, how it worked, what happened. Three paragraphs max.
Show us, don't tell us. We read every one.
*The Good Company is an equal opportunity employer.*
Pay: $50,000\.00 \- $80,000\.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Health insurance
- Life insurance
- Paid time off
- Retirement plan
- Vision insurance
Work Location: In person
Salary Context
This $50K-$80K 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 Good Company Ships, 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 ($65K) sits 70% below the category median. Disclosed range: $50K to $80K.
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.
Good Company Ships AI Hiring
Good Company Ships has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Strafford, MO, US. Compensation range: $80K - $80K.
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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