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About This Role
Description:
About the Role:
Reporting to the Director of Data \& AI Engineering, the AI/ML Engineer is a mid\-level, hands\-on technical role responsible for building and maintaining the data pipelines, AI models, and intelligent features that power the GLOBO platform. This role spans the full lifecycle of AI development—from cleaning and preparing data, to building and evaluating models, to shipping production features that directly improve operational efficiency and customer experience.
The AI/ML Engineer works across GLOBO’s modern data stack (Fivetran, dbt, Snowflake) and AI infrastructure (AWS Bedrock, LLMs, agentic frameworks) to deliver reliable, well\-tested solutions. This person is equally comfortable wrangling messy data and prompt\-engineering an LLM, and takes pride in writing clean, tested code that other engineers can build on.
Data Engineering \& Pipeline Development:
- Build and maintain reliable ingestion pipelines usingSnowflake Openflow, Python, and Snowflake, including API, PostgreSQL, and CDC\-based integrations.
- Develop incremental synchronization, cursor/state management, retry logic, schema\-drift handling, soft\-delete propagation, and source\-to\-target reconciliation.
- Transform raw source data through staging, intermediate, and core models into trusted datasets for analytics, reporting, and machine\-learning workloads.
- Apply data\-quality checks for freshness, completeness, uniqueness, referential integrity, valid relationships, and business\-rule compliance.
- Maintain source definitions, model documentation, lineage, metadata, and data contracts.
- Collaborate with data owners to ensure PII/PHI classification, masking, retention, and deletion requirements are implemented throughout the pipeline.
Model Development, Testing \& Evaluation:
- Implement monitoring and alerting for ingestion failures, pipeline freshness, schema changes, data\-quality failures, transformation errors, model drift, and inference degradation.
- Establish automated regression testing for dbt models, features, evaluation datasets, prompts, and model outputs.
- Validate that sensitive data is appropriately masked, redacted, access\-controlled, and excluded from unauthorized model training or data\-sharing workflows.
- Build safeguards for PII/PHI in recorded\-call, transcript, and AI/ML processing pipelines, including verification that redaction and deletion workflows complete successfully.
- Ensure AI/ML outputs are traceable to their source data, model or prompt version, feature set, and evaluation results.
- Define recovery procedures, data\-quality escalation paths, and operational runbooks for critical pipelines and models.
- Support human review and approval for model outputs that may affect customers, interpreters, employees, financial activity, or service quality.
Feature Development \& Integration:
- Collaborate with Product and Engineering to ship AI\-powered features into the GLOBO platform.
- Build and deploy LLM integrations (AWS Bedrock, Anthropic Claude) and agentic workflows (CrewAI, LangChain).
- Write production\-quality code with proper tests, documentation, and error handling.
Reliability \& Safety:
- Implement guardrails, monitoring, and alerting for AI services in production.
- Ensure AI outputs are consistent and trustworthy.
- Contribute to evaluation datasets, prompt versioning, and regression testing for deployed models.
Performance \& Cost Optimization:
- Monitor and optimizeSnowflake compute, storage, query performance, dbt execution, Openflow runtime usage, and model\-inference costs.
- Design efficient incremental models, CDC pipelines, materializations, clustering strategies, and warehouse/task schedules.
- Compare and optimize ingestion costs as Globo transitions from Fivetran to Snowflake Openflow.
- Reduce unnecessary full refreshes, duplicate processing, excessive data movement, and inefficient feature recomputation.
- Optimize model selection, prompt size, token usage, batching, caching, inference frequency, and routing between model providers.
- Measure model performance against operational cost, latency, throughput, and data\-freshness requirements.
- Establish practical service\-level targets for critical datasets, transformations, batch jobs, and model\-serving workflows.
Requirements:
Required Minimum Education and Experience:
- Bachelor’s Degree in Computer Science, Data Science, Information Systems, or related field.
- 2\+ years of experience in data engineering, software development, or ML engineering.
- Experience with the below tech stack is required:
+ Python (advanced proficiency)
+ SQL (advanced proficiency)
+ LLM Integration (AWS Bedrock, Anthropic Claude, or OpenAI API)
+ dbt (data transformation and testing)
+ Snowflake (or similar cloud data warehouse)
+ AWS Lambda / Serverless architecture
- Experience with the below tech stack is preferred:
+ Fivetran (or similar ELT/ingestion tooling)
+ Agentic Frameworks (CrewAI, LangChain, or similar)
+ Airflow (or similar workflow orchestration)
+ Vector Databases (Pinecone, PGVector, or OpenSearch)
+ AWS ECS/EKS
+ CDK and CloudFormation for automated deployments
+ Ruby on Rails (ability to read/debug core platform code)
+ Redis
+ PostgreSQL
+ React
- Familiarity with model evaluation techniques, prompt engineering, and AI safety best practices.
- Experience with Google Docs and Apple/Mac Operating System preferred
Additional Preferred Requirements:
- Ability to work independently in a decentralized environment without the reliance on direct authority
- Highest level of personal and professional integrity and ethics
- Broad understanding of current and emerging technology practices
- High level of initiative, accountability, and follow\-through
- Value strong teamwork and collaboration skills
- Demonstrated problem\-solving and decision\-making skills
- Ability to manage multiple initiatives and projects and prioritize needs
- Strong sense of service and passion for the company and business
- Authorized to legally work for any employer in the United States
- Willingness to submit to any requested background checks
- Fluent in English
About GLOBO:GLOBO is a B2B communication platform provider, specializing in translation and interpretation technology, services, data, and insights. For the third year in a row, GLOBO has been ranked in the top\-10 on the Philadelphia 100 list of fastest\-growing privately held companies.What’s it like to work here? We’re a close\-knit team with big ideas and ambitions. We make the impossible happen, and make hard tasks easier. We don’t take ourselves too seriously, but we’re serious about our mission—helping people communicate when it matters most.
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 GLOBO, 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.
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.
GLOBO AI Hiring
GLOBO has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 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 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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