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About This Role
A leader in technology and innovation, Southwire Company, LLC is one of North America’s largest wire and cable producers. Southwire and its subsidiaries manufacture building wire and cable, utility products, metal-clad cable, portable and electronic cord products, OEM wire products and engineered products. In addition, Southwire supplies assembled products, contractor equipment, electrical components, hand tools and jobsite power and lighting solutions. The company also offers digital solutions including contractor planning and utility grid resiliency as well as field services including cable testing, rejuvenation and replacement to support our customers as a value-add partner.
The purpose of Southwire’s College Programs is to provide valuable experience to students pursuing a bachelor’s degree or higher. Our goal is to give students meaningful work that builds on what they are learning in the classroom. Our programs are project based. Students will receive a project at the beginning of the summer and present it in our project expo at the end of the summer. Our more than seven decades of progressive growth can be attributed to our determination to develop innovative systems and solutions, exercising environmental stewardship and enhancing the well-being of the communities in which we work and live. How will you power what’s possible?
Location: Atlanta Battery
Shift: May 17th - Aug 6th
Pay Rate: $20.00 – $23.00
Job Description
Job Summary/Objective:
The purpose of Southwire’s College Programs is to help early career professionals bring together formal learning and
productive work to improve their ability to transition from school to work. Southwire provides interns and co-ops with
“real world” applications of their academic studies, helpful feedback and coaching, and consideration for employment
after graduation. Each student will have a variety of responsibilities and assigned projects that relate to their field of
study and be responsible for developing a timeline, progress updates, and final delivery in a senior leadership expo.
Note: All new interns and co-ops must begin in the summer rotation.
Ready to explore new horizons in your own development as a Southwire Intern or Co-op and help us power what’s possible?
Key Responsibilities
- Your intern assignment is generally 10-12 weeks, and co-op assignment is generally 16-18 weeks and
will include an assigned project and opportunities to present your work.
- You will also collaborate on other exciting projects and work with talented peers and inspirational
leaders, many of whom began as interns and co-ops themselves.
- You will be provided a robust developmental experience through an immersive onboarding and
offboarding experience, assigned a mentor, networking opportunities, organized events, activities, and
community service /volunteer initiatives.
- You will have the opportunity to impact the future of the organization, all the while being exposed to
a world-class program community.
- Finally, our rising senior interns and co-ops are considered for full-time employment opportunities.
Long Description
The AI/ML Engineer Intern designs, builds, and deploys machine learning systems with flexible hours. Responsibilities include using deep learning, NLP, LLM/RAG, computer vision, chatbots, RestAPI integrations, and robotics to improve business results and support innovation. This role involves collaborating with a multidisciplinary team to advance the company's AI strategy and develop early-stage solutions. This position offers an opportunity to leverage new AI trends and make practical impacts and produce business outcomes.
Essential Duties and Responsibilities
- Data Preparation and Analysis: Using Python and other programming languages/tools, will be Collecting, cleaning, preprocessing, and transforming large datasets to ensure quality and relevance for model training.
- Model Development and Optimization: Using Python and other programming languages/tools, will be designing, building, training, and testing, ML models. Deploy these models in production environment. Utilize algorithms using frameworks like TensorFlow, Keras, etc.
- System Integration: Collaborating with cross functional teams to integrate AI/ML models into existing applications and APIs including RestAPIs. using deep learning, neuro-linguistic programming (NLP), LLM/RAG, computer vision, chatbots, RestAPI integrations, and robotics will be able to solve business challenges.
- Deployment and Monitoring: Assisting in the deployment, monitoring, and maintenance of models in production environments (often cloud-based platforms like Google Cloud and Azure).
- Experimentation and Documentation: Running AI system experiments, analyzing results, documenting findings, and refining models based on performance metrics.
- Collaboration and Communication: Working with cross-functional teams and stakeholders to understand business problems and communicate technical concepts and results effectively to both technical and non-technical audiences. Involved in multiple project technical support activities.
- Staying Current: Keeping abreast of the latest AI/ML trends, tools, and best practices, including generative AI and MLOps.
Qualifications and Experience
- Studying computer science, statistics, mathematics, or a related field, with a master's degree often preferred.
- Area of study / expertise: Strong proficiency in programming languages such as Python (including libraries like pandas, NumPy, and scikit-learn etc) and SQL. Experience with other languages like R or Java is a plus.
- Strong foundation in mathematicsand statistics (probability, linear algebra, algorithms); experience with ML frameworks and libraries(TensorFlow, PyTorch, Keras, Scikit-learn; Clear understanding of MLOps practices and the full ML lifecycle
- AI/ML Expertise: Solid understanding of machine learning models and deep learning architectures (e.g., neural networks, decision trees) and familiarity with frameworks like TensorFlow, PyTorch, and Keras.
- Familiarity with cloud computing platforms (e.g., Google Cloud, Azure) for model deployment and scaling
- Certifications Preferred: Visualization Focus, ML or Generative AI Focus, Cloud and scaling focus (Gemini, Vertex AI preferably)
- Strong problem-solving and analytical skills; effective time management and self-discipline (crucial for part-time, flexible roles); excellent communication and collaboration skills; curiosity and a desire for continuous learning.
- Technical skills: Python, SQL, VB Script, Java, PowerShell, Cloud shell (Unix-Linux), Proficient with Microsoft tools (e.g. Co-pilot), ABBYY, C#, .Net, SOAP & REST API, R, Vertex AI
Benefits We Offer:
- 401k with Matching & Pension Plan (Eligible for Pension after 1,000 Hours Worked)
- Flexible Work Schedules
Southwire is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.
Salary Context
This $41K-$47K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Southwire, 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 $212,000 based on 11,866 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,408. This role's midpoint ($44K) sits 79% below the category median. Disclosed range: $41K to $47K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Southwire AI Hiring
Southwire has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US. Compensation range: $47K - $47K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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