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About This Role
Grow with us!
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- This role is a hybrid position with the expectation to work onsite (3\) three days a week in the Ericsson office located in either Plano, Texas or Boise, Idaho and relocation is not offered for this position.
- *Ericsson Enterprise Wireless Solutions Inc. does not sponsor U.S work authorizations for this job position including U.S. immigration filings for initial and/or change of employer paperwork for H\-1’s, H\-1B1’s, E\-3’s, O\-1’s, and TN’s. Ericsson also does not hire F\-1’s working on CPT or EAD for this position.*
About the Team
The Finance AI \& Analytics team is transforming finance into a strategic function through AI. We build the AI agents, applications, and automations that Ericsson’s executive leadership — including the CFO and CEO — use to steer the business. Our work doesn’t sit on a shelf: it is queried, questioned, and relied on daily at the highest levels of the company.
The Role
This is a builder’s role. As Senior AI Analytics Engineer, you will design and ship the AI agents, applications, and automations that change how our organization works — agents that answer executive questions in seconds, applications that replace weeks of manual reporting, and automations that quietly eliminate entire categories of busywork.
You’ll sit closer to the business than a traditional data engineer. The job starts with a business problem, not a pipeline: understand the question, find the signal in the data, and turn it into something people use every day. AI coding tools like Claude Code and Codex are how we work — you’ll prototype in hours, iterate with stakeholders in days, and ship in weeks.
You do not need a finance background. You’ll partner with a technically strong finance team who bring the domain depth and validate the business logic with you — you bring the analytical thinking and the ability to build. As a senior member of the team, you’ll have the authority to design and deliver solutions as you see fit, in alignment with our IT Center of Excellence.
What you will do
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- Build AI agents — Design, build, and continuously improve the AI agents our executives and finance teams rely on for answers
- Ship applications \& automations — Turn manual, repetitive workflows into applications and automations that run without you
- Deliver insight — Take ambiguous business questions and drive them to clear, decision\-ready answers backed by sound analysis
- Business partnership — Embed with stakeholders to find the highest\-value problems, then own the solution end to end
- Analytics engineering — Shape the data models and semantic views that ground our agents’ answers — we use dbt on Snowflake, and we’ll teach you the stack if it’s new to you
- Raise the bar — Set the standard for how the team uses AI tooling to build faster and better, in alignment with the IT Center of Excellence
The skills you bring
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- Hands\-on experience with AI coding tools such as Claude Code, Codex, or similar — these are core to how we build, not a nice\-to\-have
- Automation or agentic projects you can show us — personal or professional (see “Show Us What You’ve Built” below)
- Expert SQL and strong Python, with a solid analytics foundation — you know how to interrogate data and how to tell when an answer is wrong
- Experience building applications, automations, or agentic workflows that real users depend on
- A track record of partnering directly with business stakeholders to turn ambiguous needs into working solutions
The judgment and autonomy expected of a senior engineer — you decide how to solve the problem and you own the outcome
Nice to Have
- dbt experience — modeling, testing, and documentation. We’d love it, but we don’t require it
- Experience with Snowflake or a comparable cloud data warehouse
- Experience with semantic layers or semantic views that serve AI systems (retrieval, tool use)
- Streamlit, workflow automation platforms, or agent frameworks
- Finance domain exposure — helpful, but explicitly not required
Show Us What You’ve Built
The strongest applications include the things you’ve built. When you apply, share your automation or agentic projects — personal or professional, polished or vibe\-coded. A link, a repo, a demo video, or a short write\-up all work. We care far more about what you’ve shipped and what it does for its users than how pretty the code is.
Why This Role Matters
Finance runs on thousands of hours of manual work that no longer needs to be manual. Every agent, application, and automation this team ships gives that time back and changes how decisions are made at the top of the company. This role is where that change gets built.
Why join Ericsson?
At Ericsson, you´ll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what´s possible. To build solutions never seen before to some of the world’s toughest problems. You´ll be challenged, but you won’t be alone. You´ll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next.
What happens once you apply?
Click Here to find all you need to know about what our typical hiring process looks like.
Ericsson uses a merit\-based hiring approach that values people with different experiences, perspectives and skillsets. We truly believe this approach drives innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity employer, learn more.
If you need assistance or to request an accommodation due to a disability, please contact Ericsson at hr.direct.americas@ericsson.com.
DISCLAIMER: The above statements are intended to describe the general nature and level of work being performed by employees in this position. They are not an exhaustive list of all responsibilities, duties and skills required for this position, and you may be required to perform additional job tasks as assigned.
Primary country and city: United States (US) \|\| Hybrid: Plano, Texas or Boise, Idaho
Job details: IT Data Engineer
Compensation and Benefits at Ericsson
At Ericsson, we know that our people are the key to our success. We offer a competitive package to help with your individual needs and goals.
Your Pay
The salary range for this position is dependent on various factors including, but not limited to, location, and the candidate’s combination of job\-related knowledge, qualifications, skills, education, training, and experience.
Short\-Term Variable Compensation Plan: Your pay also includes the opportunity for an annual bonus. Actual bonus payouts are based on performance of the business against the unit’s objectives, individual performance, and the individual bonus target. Certain eligibility and pro\-ration rules apply.
Your Health
Ericsson Enterprise Wireless Solutions offers excellent, competitive employee benefits, such as: subsidized, nationwide PPO medical benefit options including a low\-deductible Point of Service Plan and a qualifying High Deductible Health Plan (HDHP), with a generous company\-provided HSA contribution.
Your Financial Security
We invest in both your short and long\-term financial wellbeing. Our 401(k) plan has a 4% company match and immediate vesting. Employees will also receive company\-paid employee basic life and AD\&D insurance and company\-paid disability benefits.
Your Time
Your work\-life balance is important to us. New employees are provided a minimum of 15 days of accrued vacation, up to 3 personal days per year, 11 annual holidays, 8 hours of volunteer time, and 80 hours of sick time annually. Please note paid time off is pro\-rated based on the employee’s start date. Furthermore, Ericsson provides up to 16 weeks of paid maternity leave and 6 weeks of parental or adoption leave at 100% of pay.
Additional Benefits
Ericsson Enterprise Wireless Solutions offers other company\-paid benefits such as a comprehensive Employee Assistance Program, mobile therapy, and volunteer paid time off.
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 Ericsson, 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.
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.
Ericsson AI Hiring
Ericsson has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Boise, ID, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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