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About This Role
Who we are:
Hi, we’re SambaSafety and we offer the industry’s most comprehensive driver monitoring software. Our mission is promoting safer communities by reducing risk through data insights. Companies trust SambaSafety to keep their employees safe on the roads, price and reduce risk, help protect their brand, their bottom line, and our global community.
We’ve built an inclusive, supportive, and exceptional culture where every employee is empowered in their role. Don’t take our word for it; we’ve been recognized as a Top Workplace by The Denver Post, Albuquerque Journal, Sacramento Bee, and Built In Colorado. And our employees rate SambaSafety as top\-notch, with a rock solid Top Rating on Glassdoor.
What You’ll Do:
As a Senior Business Intelligence Analyst on SambaSafety’s Enterprise Data Team, you will sit at the intersection of traditional BI and modern AI\-driven analytics. You’ll build the dashboards, semantic layers, and AI\-ready data foundations that let business users — and increasingly, AI agents — get trustworthy answers fast. This role is for someone who wants to actively use AI tools in their daily development workflow (not just report on AI as a topic), while applying rigorous data quality and governance discipline to make sure AI\-generated insights are accurate, explainable, and trusted.
Responsibilities:
AI\-Augmented Analytics \& Development
- Design and build AI\-augmented dashboards and self\-service analytics experiences using Power BI and Looker, including natural\-language query interfaces backed by Semantic Views
- Use AI\-assisted development tools (e.g., Claude Code, GitHub Copilot, ChatGPT) daily to accelerate SQL development, dashboard scaffolding, testing, and documentation
- Prototype and evaluate GenAI use cases — automated insight generation, anomaly narratives, AI\-written executive summaries — to reduce manual reporting effort
- Partner with data engineering and data science to productionize forecasting, anomaly detection, and clustering outputs into BI dashboards business users can act on
- Write Python for advanced analysis, automation, and integration with LLM/AI APIs where SQL and point\-and\-click BI tools aren’t enough
AI\-Ready Data Foundation
- Build and maintain AI\-ready metadata (table/column descriptions, glossary terms, certified lineage) in OpenMetadata that powers natural\-language\-to\-SQL agents and other AI query tools
- Guide and advise engineering teams on the design and adoption of Semantic Views so business terms map cleanly to underlying tables for both human and AI/agent consumption
- Define, monitor, and enforce data quality rules (completeness, accuracy, consistency, timeliness) so AI\-generated outputs are built on trusted data, not silently wrong data
- Partner with data engineering on metadata cataloging efforts that trace lineage from BI reports back to source Snowflake tables, closing gaps that block AI/analytics reliability
Core BI \& Business Partnership
- Explore data to discover patterns, relationships, anomalies, and trends, applying structured, hypothesis\-driven analysis increasingly augmented by AI/ML\-based approaches
- Gain a deep understanding of core business processes and align data/AI development with business strategy
- Prepare reports, presentations, and dashboards that translate AI\-driven and traditional analytics findings into clear, fact\-based recommendations for the next steps
- Analyze testing results to ensure BI and AI\-assisted solutions meet business needs; recommend standards, policies, and procedures for BI tools, systems, and responsible AI use
- Present insights to stakeholders up to the C\-suite, translating technical AI/analytics outputs into language business decision\-makers can act on
What You’ll Need:
- BS in Computer Science, Software Engineering, or a related discipline, or relevant work experience; exposure to the software development lifecycle (SDLC) is a plus
- 5\+ years working in data\-related roles, 2\+ years involved in company(s) Business Intelligence processes
- Expert\-level SQL across different RDBMS technologies, plus working knowledge of Python for data analysis and automation
- Experience with the Modern Data Stack — Snowflake, Power BI, Looker, and ETL tools; exposure to other Business Intelligence platforms is a plus
- Hands\-on experience with AI\-assisted development tools (e.g., Claude Code, GitHub Copilot, ChatGPT) in a real development workflow — not just casual use
- Experience with or strong interest in LLM\-powered analytics: natural\-language\-to\-SQL, RAG\-based query agents, or Semantic Views
- Comfortable acting as the business/semantic\-modeling voice in conversations with data engineering — translating business definitions into Semantic View design guidance rather than writing the underlying pipeline code
- Working understanding of prompt engineering and how to validate/QA AI\-generated outputs before they reach business users
- Experience defining and applying data quality frameworks — profiling, validation rules, monitoring — to ensure trusted, decision\-ready data
- Familiarity with applied ML concepts (forecasting, anomaly detection, clustering) — you don’t need to build models, but you should be comfortable using and explaining their outputs
- Experience with data governance/catalog tools such as OpenMetadata or Atlan is a plus
- Experience with NO\-SQL databases and large\-scale data is a plus
- Experience working with C\-suite, product teams, and business users to understand how to optimally deliver insights within their operational workflows and decision\-making processes
- Knowledge of open\-source reporting tools like Redash is a plus
Benefits and Perks:
- Flexible and generous Paid Time Off and Paid Volunteer Days
- 401k Employer Match
- Generous Healthcare Benefits
- Up to 12 weeks paid time off for maternity leave based on tenure
- Wellness \&Tuition Reimbursement
- Flexible Work Arrangements
- Lots of SambaSafety swag \& SambaSafety Events
Our team of talented and committed safety professionals is exceptional. At SambaSafety we strive to foster an inclusive culture that supports, encourages and celebrates a wide array of diversity. We are committed to create a space where all employees can show up as their authentic selves every day, and we work to advance employee equality, diversity and inclusion.
SambaSafety provides equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, gender identity, and expression or genetics.
Come join us to find out for yourself what all the excitement is about!
Salary Context
This $100K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At SambaSafety, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($115K) sits 47% below the category median. Disclosed range: $100K to $130K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
SambaSafety AI Hiring
SambaSafety has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $130K - $130K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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