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About This Role
Position Overview
As an AI Engineer at Better Direct, you will own AI-powered features from concept through production—including the model, data pipeline, API, and user-facing experience. You will work across AI/ML systems and our application stack to turn complex documents and business workflows into reliable tools used by sales and engineering teams.
Key Responsibilities
- Build document-processing pipelines that transform RFQ PDFs, scanned pages, spreadsheets, and emails into structured data such as part numbers, quantities, manufacturers, and shipping addresses.
- Run, optimize, and monitor self-hosted LLMs, vision-language models, OCR systems, embedding models, and smaller task-specific models on on-premises NVIDIA GPUs and a Kubernetes-based AI cluster.
- Build search and RAG systems for contracts, product catalogs, and historical quotes using PostgreSQL, pgvector, BM25, vector retrieval, reranking, and deduplication.
- Develop tool-using agents that research products across distributor catalogs—including TD SYNNEX, Ingram Micro, and D&H—and prepare quotes for human review.
- Train and evaluate classifiers, NER models, sequence-labeling models, and other efficient alternatives when large language models are too slow or expensive.
- Create labeled evaluation datasets, regression tests, and performance benchmarks covering accuracy, recall, latency, reliability, and cost.
- Build and maintain Python services using FastAPI, asynchronous processing, Celery, and Redis for extraction, crawling, polling, and other background workloads.
- Ship AI capabilities into the CRM through Express and Prisma APIs, PostgreSQL schema changes and migrations, and React interfaces.
- Integrate AI output into production workflows using confidence scores, human review, feedback capture, permissions, and audit trails.
- Deploy and maintain services using Docker, Git, and documented runbooks. Monitor production jobs, diagnose failures, and support features after release.
- Write clear pull requests, design notes, test plans, and handoff documentation so other engineers can understand and maintain your work.
Required Qualifications
- U.S. citizenship.
- At least one year of experience building applications powered by LLMs or other machine-learning models that were used in real workflows.
- Strong Python skills, including experience with FastAPI or a similar framework, asynchronous programming, packaging, and automated testing.
- Working knowledge of TypeScript or JavaScript and experience building REST APIs or React applications, or the ability to learn these technologies quickly.
- Strong SQL and PostgreSQL fundamentals, including schema design, migrations, indexing, and query optimization.
- Hands-on experience in at least two of the following areas:
- RAG, embeddings, or semantic search
- Document extraction or OCR
- Agent tools, prompting, or workflow orchestration
- Model fine-tuning or training
- Model serving with vLLM, SGLang, or a similar platform
- Experience creating labeled test sets, selecting appropriate metrics, and objectively measuring whether a change improved performance.
- Comfort working with Linux, Git, and Docker.
- Clear written and verbal communication, including the ability to explain technical trade-offs to non-technical stakeholders.
AI-Assisted Engineering
AI-assisted development is part of how we work. Engineers may use tools such as OpenAI Codex to research unfamiliar systems, explore solutions, write code, generate tests, debug problems, and produce documentation.
These tools support engineering work, but responsibility for the final result remains with the engineer. You should be able to:
- Turn unclear business requests into well-defined technical tasks and acceptance criteria.
- Give AI tools the appropriate context, constraints, and technical requirements.
- Read, understand, explain, and maintain all code you submit.
- Recognize incomplete, unsafe, inaccurate, or unnecessarily complex generated solutions.
- Verify assumptions against the existing codebase, database, architecture, and application behavior.
- Evaluate trade-offs and select practical, maintainable solutions.
- Test permissions, security, data integrity, performance, failure modes, and edge cases.
- Use approved tools responsibly when working with company and customer data.
- Troubleshoot and support your work after it is released.
Preferred Qualifications
- Experience running open-weight models such as Qwen, Llama, or Mistral on self-managed GPUs.
- Knowledge of quantization, multi-GPU inference, model optimization, and NVIDIA driver or CUDA troubleshooting.
- Kubernetes experience, particularly with KServe or another model-serving platform.
- Experience using vision-language models or OCR frameworks such as Docling for document understanding.
- Background in traditional NLP techniques such as text classification, named-entity recognition, active learning, weak supervision, or conditional random fields.
- Experience with Celery and Redis, Selenium or Playwright, or S3-compatible storage such as MinIO.
- Familiarity with IT hardware and software products, manufacturer part numbers, and distributor catalogs.
- Contributions to open-source machine-learning or developer-tooling projects.
What We Value
- Systems thinking: Understanding how models, services, databases, interfaces, and integrations work together.
- Data correctness: Preventing duplicate, missing, inconsistent, or unauthorized changes.
- Practical problem-solving: Identifying root causes and comparing solutions based on evidence.
- Production ownership: Validating releases, monitoring results, and responding when problems occur.
- Scalability and maintainability: Recognizing designs that may become slow, fragile, or difficult to extend.
- Business understanding: Learning why a workflow exists, not simply how to implement it.
- Good judgment: Knowing when to proceed, investigate further, simplify the approach, or ask for help.
- Continuous learning: Developing new skills quickly and steadily taking on greater technical ownership.
We do not expect an AI Engineer to know every technology on the first day. We do expect curiosity, disciplined thinking, honest evaluation, and a willingness to take responsibility for production outcomes.
Compensation and Benefits
Salary range: $60,000–$70,000 per year, based on experience.
Better Direct is an equal opportunity employer under applicable law.
Pay: $60,000.00 - $70,000.00 per year
Benefits:
- 401(k)
- Dental insurance
- Health insurance
- Life insurance
- Paid time off
- Professional development assistance
- Retirement plan
- Vision insurance
Work Location: In person
Salary Context
This $60K-$70K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Better Direct, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($65K) sits 69% below the category median. Disclosed range: $60K to $70K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Better Direct AI Hiring
Better Direct has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Tempe, AZ, US. Compensation range: $70K - $70K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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