Interested in this AI/ML Engineer role at Postali LLC?
Apply Now →Skills & Technologies
About This Role
At Postali, we believe the strongest teams will pair expert human judgment with intelligent systems. We have already begun building AI\-assisted workflows across content, search, research, reporting analysis, and website publishing. The next step is to make those systems dependable, measurable and genuinely useful in day\-to\-day work.
We are looking for an AI\-native builder who uses AI as a problem\-solving medium, not simply as a writing tool. If you naturally decompose messy work, design the right combination of agents, automation and human judgment, and keep improving the system after it reaches production, this may be the right role for you.
About the Role
The AI Systems Engineer owns the design, development, and day\-to\-day operation of Postali’s agentic AI systems. This is a hands\-on, individual\-contributor role for someone who can move from an ambiguous business problem to a working, monitored system that people use.
You will partner across content, SEO, client performance, paid media, development and operations. Your job is to understand how work is actually done, decide what should be handled or supported by AI and build the workflow that makes the result faster, more consistent or more capable.
What You’ll Do
- Own Postali’s portfolio of AI agents and AI\-enabled workflows across content, SEO, research, reporting analysis, publishing support and internal operations.
- Find high\-value problems worth solving. Map the current process, identify the decisions and failure points, and choose the right combination of LLMs, deterministic logic, automation and human approval.
- Design and maintain reusable agent skills, prompts, context files, knowledge and retrieval systems, structured outputs, tool connections and client\-specific configurations.
- Improve Postali’s AI content system, including research, drafting, client context, factual and citation checks, brand and legal\-quality controls, editorial handoffs and repeatable setup for new clients.
- Create agentic workflows that help expert teams diagnose performance, analyze competitors, synthesize data, recommend next actions and turn approved decisions into executable work.
- Engineer human\-in\-the\-loop controls. Define what the system can do autonomously, what requires expert review, and what should never be automated, especially for client\-facing claims, regulated content, sensitive data and production changes.
- Connect agents to approved tools and data through APIs, webhooks, MCP, automation platforms and light production code.
- Build evaluations before declaring a system ready. Measure accuracy, completeness, consistency, compliance, latency, cost and real\-world usefulness, and use those results to improve or retire the workflow.
- Own production reliability for AI systems, including logs, monitoring, alerts, versioning, permissions, model and token costs, fallback behavior, incident response and recovery.
- Create reusable frameworks that can scale across clients while preserving the specific context, standards and judgment each law firm requires.
- Document system architecture, models, data sources, prompts and skills, dependencies, decisions, credentials, runbooks and backup ownership.
- Train teams to use the systems effectively, observe where adoption or trust breaks down, and improve both the technology and the operating process.
- Stay current through disciplined experimentation. Test new models, tools and patterns against clear use cases and evidence rather than adopting technology for its own sake.
Why This Role Is Different
Many AI roles focus on strategy decks, isolated demos or tool recommendations. This role owns systems that colleagues depend on in real work.
You will have room to experiment, but you will also be accountable for what happens after the prototype: evaluation, adoption, quality, reliability, cost, documentation and measurable business impact. The goal is not more AI activity. It is better systems and better work.
What We’re Looking For
- A track record of personally building and operating AI, automation, software or data systems that other people use. Titles and exact years are flexible; evidence of thoughtful, production\-minded work matters more.
- You are genuinely AI\-native. You can show how AI has changed the way you research, reason, build, debug and solve problems.
- Hands\-on experience with LLM\-based agents, tool calling, structured outputs, retrieval or knowledge systems, context engineering, evaluations and human\-approval workflows.
- Clear communication and change leadership. You can explain an AI system to technical and nontechnical colleagues, write usable documentation, and help a team adopt a new way of working.
- Practical integration ability with APIs, webhooks, authentication, JSON and automation platforms. You know when no\-code is sufficient and when code is the more reliable choice.
- Experience with MCP, vector or hybrid retrieval, orchestration frameworks, workflow automation platforms and observability for AI systems.
- Production judgment. You think about hallucinations, data quality, privacy, security, permissions, failure modes, model drift, cost and maintainability before calling a system finished.
- A strong systems and measurement mindset. You define the outcome, the evaluation and the owner before building, then use evidence to decide whether to improve, replace or stop the system.
Great to Have
- A marketing, agency or client\-service background, especially experience with content, SEO, analytics, paid media or website\-publishing workflows.
- Experience in legal marketing or another regulated or high\-trust environment.
- Experience designing multi\-client or multi\-tenant systems with reusable shared logic and client\-specific configurations.
- Strong data literacy, including comfort working with spreadsheets, analytics outputs and imperfect business data.
- Comfort writing, understanding and debugging light production code, preferably in Python or JavaScript/TypeScript, and using Git\-based workflows and AI coding tools responsibly.
The Tools You’ll Work With
Claude and other leading LLMs, model APIs, MCP, Python or JavaScript/TypeScript, GitHub, structured data and retrieval systems, Workflow Automation Platforms (N8N, Zapier, etc), Wrike and Notion, and Postali’s connected marketing stack. WordPress, AgencyAnalytics and CRM platforms will often be systems your agents interact with.
Job Details
- Full\-time, hybrid position.
- Postali’s office is in Columbus, Ohio.
- Compensation: $110,000 \- $125,000
- Benefits include 401(k); health, dental and vision insurance; life insurance; generous paid time off; and 12 weeks of paid parental leave.
How to Apply
You do not need to check every box. If you are an AI\-native builder who wants to turn real business problems into reliable systems, we want to hear from you. Submit an application through Postali’s careers page and include a portfolio, case study or clear description of an AI system you personally designed and built.
Pay: $110,000\.00 \- $125,000\.00 per year
Benefits:
- 401(k)
- Dental insurance
- Health insurance
- Life insurance
- Paid time off
- Parental leave
- Retirement plan
- Vision insurance
Ability to Commute:
- Columbus, OH 43215 (Preferred)
Work Location: Hybrid remote in Columbus, OH 43215
Salary Context
This $110K-$125K 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 Postali LLC, 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 ($117K) sits 45% below the category median. Disclosed range: $110K to $125K.
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.
Postali LLC AI Hiring
Postali LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Columbus, OH, US. Compensation range: $125K - $125K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.