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About This Role
Role Summary
Direct Agents is running a growing fleet of AI-powered systems across our client and internal reporting stack — automated account agents, intelligence systems, always-on client dashboards — and is now building a new, confidential generative AI product for the media industry.
We're hiring a full-time, remote Full-Stack Developer to do both: help keep the existing fleet running and improving, and help build the new product from the ground up. A meaningful part of this job is working inside Claude Code as our primary engineering environment — steering and reviewing AI-generated code and agent workflows, not only writing everything by hand.
What You'll Work On
Live systems you'll help support and extend:
- Internal agent platform — syncs and synthesizes account data, call transcripts, and performance signals across every client account, surfacing findings automatically
- Client-facing report sites and dashboards — hosted, JavaScript front ends backed by a SQL data layer and marketing-data pipelines, live for a dozen-plus client and internal audiences
- Workflow automations — connecting data sources, chat, and email/calendar tools so reporting and account data move without manual steps
Plus:
- The confidential new product — a from-scratch generative AI build for the media industry, where you'll help shape architecture and features from day one
Key Responsibilities
- Build and maintain full-stack features across our Python/FastAPI backends, JavaScript front ends (Vue preferred), and SQL data layer
- Work inside Claude Code daily: prompt and steer agentic coding workflows, review AI-generated code and pull requests for correctness, and extend our library of custom skills and subagents
- Debug and harden the data pipelines (marketing-data extracts, SQL views, workflow automations) that feed client dashboards and internal agents
- Integrate LLM APIs (Claude, GPT) for generative features — copy generation, insight extraction, creative/image analysis — in both the existing systems and the new product
- Own deploys for live client-facing sites and internal tools, including monitoring, rollback, and incident response
- Collaborate with data, strategy, and account teams to translate reporting and media needs into working software
- Document systems, prompts, and workflows so institutional knowledge isn't locked in one person's head
- Contribute to architecture and feature decisions on the new confidential product alongside day-to-day support work
Our Stack & Tools
- Primary dev environment: Claude Code — agentic coding, subagent workflows, custom skills. Comfort working AI-assisted (directing and reviewing agent output, not only typing every line by hand) is a core requirement for this role, not a bonus skill.
- Backend: Python, FastAPI, SQL (SQL Server / Azure SQL)
- Frontend: Vue.js (React or Angular experience also useful — several of our tools are React)
- Data & pipelines: marketing-data platforms, workflow automation tools (e.g. n8n), SQL databases
- Deployment & infra: Netlify, containerized cloud services, Git-based version control
- AI: Claude and GPT APIs, retrieval/vector search, agent orchestration patterns
Qualifications
- Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent experience)
- Proven full-stack development experience, with genuine interest or hands-on experience in AI-assisted or agentic development — not just calling an LLM API, but working inside tools like Claude Code, Cursor, or similar
- Solid backend skills in Python; front-end proficiency in JavaScript, Vue preferred
- Familiarity with LLM APIs, vector search, or media/marketing AI workflows is a strong plus
- Experience with RESTful APIs, Git-based version control, and agile workflows
- Comfortable owning both greenfield build work and ongoing support/maintenance of live, client-facing systems
- Excellent collaboration and communication skills — this role works directly with data, strategy, and account teams, not only engineering
Please be aware of fraudulent recruiters and scams. If you move forward in our recruitment process, you will only be contacted by the Direct Agents HR team. Questions? Email [email protected]
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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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Direct Agents, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Direct Agents AI Hiring
Direct Agents has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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