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Job Description
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Job Title: Junior Machine Learning Engineer
Working Pattern: Full\-time
Working location: Indianapolis, IN (Hybrid Schedule/3 days onsite minimum)
We’re looking for a Junior Machine Learning Engineer to join our growing team. In this role, you will tackle exciting challenges in AI/ML, software development, and Data Science. You’ll be part of a multi\-disciplinary team, working together to tackle technical challenges in a stimulating and collaborative environment. In this role, your focus will be on building and deploying tools to deliver insights on Rolls\-Royce products.
Why Rolls\-Royce?
Rolls\-Royce is one of the most enduring and iconic brands in the world and has been at the forefront of innovation for over a century. We design, build and service systems that provide critical power to customers where safety and reliability are paramount.
We are proud to be a force for progress, powering, protecting and connecting people everywhere.
We want to ensure that the excellence and ingenuity that has shaped our history continues into our future and we need people like you to come and join us on this journey.
Rolls\-Royce has been recognized as the top employer in the Engineering \& Manufacturing category on the prestigious Forbes Top Employers for Engineers list for 2025\. This ranking highlights our commitment to innovation, employee development, and fostering a collaborative environment where engineers can thrive.
Be part of a team that sets the industry standard and drives groundbreaking solutions.
At Rolls\-Royce, we are committed to creating a workplace where all employees feel respected, supported, and empowered to do their best work. We foster a welcoming and innovative work environment that invests in you, giving you access to an incredible breadth and depth of opportunities where you can grow your career and make a difference.
Rolls\-Royce is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to any protected characteristics.
What you will be doing
With this attractive opportunity you will get a chance to:
- Work with IT, and Rolls\-Royce engineering teams address key opportunities for application of digital technologies providing optimal impact and value.
- Understand, clean, and analyze large datasets from engine sensors, test rigs, and fleet operations.
- Build, train, and validate machine learning models for tasks such as searching engineering knowledge, anomaly research, production support, and predictive maintenance.
- Document your work clearly and present findings to both technical and non\-technical audiences.
Who we’re looking for:
At Rolls\-Royce we put safety first, do the right thing, keep it simple and make a difference. These principles form the behaviours that guide us and are an essential component of our assessment process. They are the fundamental qualities that we seek for all roles.
Basic Requirements:
- Bachelor’s degree in Engineering, Chemistry, Computer Science, Information Technology, Mathematics, Physics; OR
- Masters degree in Engineering, Chemistry, Computer Science, Information Technology, Mathematics, Physics, OR
- PhD in Engineering, Chemistry, Computer Science,, Information Technology, Mathematics, Physics.
- In order to be eligible for consideration, you must be a U.S. Citizen
Preferred Requirements:
- 1\+ years of Machine Learning experience which can include internships
- Experience or interest in modern software development methodologies and coding languages
- Experience or interest in developing solutions in surrogate modelling, Generative AI, or Agentic AI
- Technical and soft skills including systems thinking aptitude, communications, time management, and collaboration across multi\-disciplinary teams.
- Interest in problem\-solving and the ability to work across disciplines with people who are not data or A.I. specialists but engineer and support mechanical systems.
- Aspire to actively learn and dynamically respond to evolving objectives and emerging opportunities.
What we offer
We offer excellent development opportunities, a competitive salary, and exceptional benefits. These include bonus, employee support assistance and employee discounts.
Your needs are as unique as you are. Hybrid working is a way in which our people can balance their time between the office, home, or another remote location. It’s a locally managed and flexed informal discretionary arrangement.
As a minimum we’re all expected to attend the workplace for collaboration and other specific reasons, on average three days per week.
For fully remote roles, employees can live in any state except Idaho, Nebraska, Nevada, Vermont, and Wyoming.
Relocation assistance is available for this position.
Global Grade 8
Closing date:8/8/2026
Job Category
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Mechanical Systems
Job Posting Date
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04 Aug 2026; 00:08
Pay Range
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$66,306 \- $107,747\-Annually
Location:
Indianapolis, IN
Benefits
Rolls\-Royce provides a comprehensive and competitive Total Rewards package that includes base pay and a discretionary bonus plan. Eligible employees may have the opportunity to enroll in other benefits, including health, dental, vision, disability, life and accidental death \& dismemberment insurance; a flexible spending account; a health savings account; a 401(k) retirement savings plan with a company match; Employee Assistance Program; Paid Time Off; certain paid holidays; paid parental and family care leave; tuition reimbursement; and a long\-term incentive plan. The options available to an employee may vary depending on eligibility factors such as date of hire, employment type, and the applicability of collective bargaining agreements.
Salary Context
This $66K-$107K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Rolls-Royce, 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 in Demand for This Role
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. Entry-level AI roles across all categories have a median of $110,000. This role's midpoint ($87K) sits 60% below the category median. Disclosed range: $66K to $107K.
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.
Rolls-Royce AI Hiring
Rolls-Royce has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Indianapolis, IN, US. Compensation range: $107K - $160K.
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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