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About This Role
Overview:
TeleWorld Solutions is seeking a Machine Learning Engineer for our team! As a Performance Assurance Machine Learning Engineer, you will work under the coaching of Senior and Lead engineers of the Data Science \& Tools Team to analyze Samsung’s deployed network elements. You will utilize skills to query databases to extract data, use skills in Python or R to analyze data such that you can identify 4G/5G network infrastructure and performance issues and build prediction models, ad\-hoc tools, and dashboards to communicate your findings with your team and peers.
TeleWorld Solutions is a strategic wireless engineering and consulting firm offering network operators, OEMs and tower companies turnkey design, optimization, network dimensioning and deployment services.
With the experience of hundreds of thousands of successful implementations, including macro, DAS, Small Cells, and Wi\-Fi, the world’s leading network operators and OEMs trust our knowledge and experience to plan, perform, troubleshoot, and implement an array of technologies and solutions. *Come join our Veteran\-Friendly Team. The Company with Great Benefits and certified as "A Great Place to Work".*
Responsibilities:
- 7\+ years of professional experience in Data Science, Data Analytics, and/or Data Engineering with abilities to work with large datasets using demonstrated statistical, predictive modeling and machine learning methods.
- 3\-5 years demonstrated experience on designing, deploying, and maintaining large scale ML systems in a production environment.
- Work closely with the internal and external stakeholders to explore relationships of 4G/5G KPI measurements and targets (KPI, KQI) for 4G/5G RAN product acceptance and performance monitoring.
- Collaborate with RF engineers, network engineers, data scientists, platform engineers, product teams, and operations stakeholders to ensure ML outputs are technically accurate, interpretable, and operationally useful.
- Utilize A/B testing, statistical, and machine learning models to build robust mechanism for product \& feature performance analysis, for evaluation of new product \& SW releases and 3rd party product evaluation.
- Aid in product \& feature performance analysis, evaluation of new product \& SW releases and 3rd party product evaluation using analytics/data science to drive intelligent business decisions.
- Work with the team to proactively define and interpret data/metrics/KPIs, analyze results, and provide insights to determine operational impact, trends and opportunities for all the 4G/5G RAN products.
- Prototyping use cases, implementing automations, and developing tools to support and augment manual or repetitive efforts.
- Communicate key findings to stakeholders using visualizations and/or other suitable methods.
- Excellent verbal and written communication skills to communicate technical and complex concepts in an easy\-to\-follow progression.
- Able to compile analysis output and findings into a succinct story for technical and non\-technical audiences.
- Adapt to changes in a dynamic business environment and, support management initiatives.
Qualifications:
- Graduate Degree in Computer Science, Statistics, Data Science or a related Data Engineering with 7\+ years of professional experience is preferred.
- Programming experience: Python \& Spark (preferred) and/or other languages such as R , SQL, Hive, Spark, Javascript, Visual Basic, C\+\+, shell scripting in a linux or IDE environment such as VSCODE, Jupyter, RStudio, etc.
- Cloud Development Experience – AWS/Azure/Google utilizing cloud providers such as Databricks or Snowflake
- Machine learning expertise: GLM Regression (Linear, Logistic, Multinomial), Decision Tree (including Boosted Trees, Random Forest), kMeans/Hierarchical Clustering, Principle Component Analysis, t\-SNE, Neural Networks such as transformers and auto\-encoders, Bayesian Regression, and Times Series Modeling.
- Experience using data with high\-volume (1TB\+) \& high\-dimensionality (500\+ variables per schema), especially within a big data framework (HaDoop, Citus, MongoDB, etc).
- Experience performing Data Wrangling, Exploratory Data Analysis (EDA), Correlation Analysis, Statistical Methodologies (distributions, hypothesis testing, confidence intervals) \& Significance Testing, A/B Testing.
- Experience with MLOPS concepts and environments such as MLFLOW a plus.
- Experience with basic linux administration and software development in a linux environment.
- Experience in hardware resource management and configuration – CUDA, Docker, KubeFlow, Kubernetes, etc a plus
- Must possess qualities of being curious and eagerness to learn .
- Experience with data visualization and ability to quickly grasp statistical methods, and methodologies. Maintain a strong command of current data analytics technology trends, including emerging paradigms and practices.
- Demonstrated research and problem solving skills via prior work experience. Experience with wireless infrastructure provider and/or operator is desired.
- Technical knowledge of any wireless technology \& procedures including CDMA/EVDO/LTE/Volte and/or 5G a plus.
- Experience with evaluating service performance trends and proactively defining RAN system performance related issues a plus.
- Experience of software version control, coding best practices, and use of development management software such as github, bitbucket, etc is desired.
- Must have a strong work ethic, integrity and work extremely well independently or in a team environment.
Physical/Mental Demands and Working Conditions: The position requires the ability to perform the essential duties and responsibilities in the following environment:* Excellent interpersonal and communication skills. Must be skilled in developing and maintaining good working relationships with all appropriate levels within and outside the company.
- Operate a computer keyboard and view a video display terminal more than 75% of work time in an office work environment
Join Our Veteran\-Friendly Team:
Are you a veteran or a veteran spouse with expertise in telecommunications? Join our team at TeleWorld Solutions, where we value your military experience and provide great benefits. We invite all veterans and veteran spouses to bring their skills and dedication to our team. *TeleWorld Solutions is committed to employing a diverse workforce and provides Equal Employment Opportunity for all individuals regardless of race, color, religion, gender, age, national origin, marital status, sexual orientation, gender identity, status as a protected veteran, genetic information, status as a qualified individual with a disability, or any other characteristic protected by law.*
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 TELEWORLD SOLUTIONS INC, 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.
TELEWORLD SOLUTIONS INC AI Hiring
TELEWORLD SOLUTIONS INC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Plano, TX, US.
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
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