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About This Role
Are you looking to Optimize your life? Start your exciting path to a rewarding career today!
We are Optimum, a leader in the fast\-paced world of connectivity, and we're seeking driven and enthusiastic professionals to join our team, empower lives, fuel businesses, and drive innovation. Connectivity is now longer a luxury, but a necessity. A career at Optimum means you'll be enabling progress and enhancing lives by providing reliable, high\-speed connectivity solutions that keep the world connected. Our successes, now and in the future, are powered by our amazing product, a commitment to our people and culture, and the connections we make in our communities.
If you are resourceful, collaborative, and passionate about delivering consistent excellence, Optimum is for you!
Job Summary
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Machine Learning Engineers work to deploy end\-to\-end solutions to business problems leveraging AI and/or ML principles as needed to create those solutions. MLEs will take requests from stakeholders, define the components required for the project, gather data necessary for project EDA and training, then work with stakeholders to develop a plan around the productionized use of the solution, and work to put that solution into final production.
Responsibilities
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- Consult with stakeholders to gather business requirements, translate them into data solutions, design high\-level model structures and demonstrate deep expertise in advanced analytics techniques (e.g., AI and ML) to design, prototype, and build solutions to business problems
- Lead communication with other stakeholders to drive use case development and manage expectations on model limitations and lead times
- Analyze data to identify useful relations, patterns and features that are predictive of user behaviors, preferences, intents, interests
- Manage and execute entire projects from start to finish, including cross\-functional project management; data collection and manipulation, analysis and modeling; communication of insights and recommendations; productionalization of final model products
- Share findings with stakeholders to improve business decisions and/or influence strategic direction.
- Monitor and stay updated with industry trends and emerging technologies to identify opportunities for innovation and improvement
- Developing and maintaining the end to end modeling code and standardizing the code for reusability in the production environment.
- Profiling users including customer segmentation to help the marketing team to target specific audience for upgrading to services and also for the user retention
Qualifications
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- Degree in a quantitative discipline, such as Data Science, Applied Mathematics, Statistics, Economics, Operations Research, Computer Science, Mathematics, Physics, Biology, Chemistry or Engineering. An advanced degree, Data Science bootcamp or MOOC certification is a plus.
- 3\-5 years of work experience in classification, regression, clustering, natural language processing NLP, experiments, and optimization.
- Ability to apply Bayesian inference, frequentist statistics, causal modeling, and / or machine learning techniques.
- Experience with any of these: customer segmentation, campaign targeting and effectiveness, A/B experiments, quasi\-experiments, sales forecasting, churn propensity modeling, customer lifetime value analysis, credit risk, geospatial analytics, survey key\-drivers, marketing mix modeling, multi\-touch attribution, or recommender systems.
- Highly skilled in R and Python for statistical and machine learning programming.
- Highly skilled in SQL \& Python coding to wrangle and explore structured \& unstructured data.
- Proficient with server or Cloud computing platforms, such as Google Compute Engine or EC2\.
- Proficient with data warehouses, such as Oracle, Big Query, or AWS.
- Subject matter scientist that can review the literature to identify state of the art solutions to a business problem.
At Optimum, every action and interaction we take part in, is driven by our three Guiding Principles: Do What’s Right, Drive One Optimum, and Make It Happen. These aren’t just words, they help us build trust, create real community, and embrace new ways of thinking. Our employees are empowered to do the right thing for our customers and co\-workers and to recognize and reward these behaviors when we see them. It’s all part of the bigger picture of “Be The Difference” where each employee knows they have the power to enact real change, share new ideas, and understand that learning never stop.
If you have the drive to succeed and are ready to embark on a thrilling career, seize this opportunity today, and join our winning team. Together, we'll shape the future of connectivity.
All job descriptions and required skills, qualifications and responsibilities for a particular position are subject to modification by the Company from time to time, in the Company’s discretion based on business necessity.
We are an Equal Opportunity Employer committed to recruiting, hiring and promoting qualified people of all backgrounds regardless of gender, race, color, creed, national origin, religion, age, marital status, pregnancy, physical or mental disability, sexual orientation, gender identity, military or veteran status, or any other basis protected by federal, state, or local law.
The Company collects personal information about its applicants for employment that may include personal identifiers, professional or employment related information, photos, education information and/or protected classifications under federal and state law. This information is collected for employment purposes, including identification, work authorization, FCRA\-compliant background screening, human resource administration and compliance with federal, state and local law.
Applicants for employment with The Company will never be asked to provide money (even if reimbursable) as part of the job application or hiring process. Please review our Fraud FAQ for further details.
Pay is competitive and based on a number of job\-related factors, including skills and experience. The starting pay rate/range at time of hire for this position in New York is $156,774\.00 \- $198,273\.00 / year. The starting pay rate/range at time of hire for this position in Plano, Texas is $130,645\.00 \- $165,228\.00 / year. For other locations, please inquire with your recruiter. The rates/ranges provided herein are the anticipated pay at the time of hire, and do not reflect future job opportunity.
Salary Context
This $130K-$198K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Optimum, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($164K) sits 9% below the category median. Disclosed range: $130K to $198K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Optimum AI Hiring
Optimum has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Long Island City, NY, US. Compensation range: $198K - $198K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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