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About This Role
### About Radformation
Radformation is transforming the way cancer clinics deliver care. Our innovative software automates and standardizes radiation oncology workflows, enabling clinicians to plan and deliver treatments faster, safer, and more consistently, so patients everywhere can receive the same high\-quality care.
Our software focuses on three key areas:
- Time savings through automation.
- Error reduction through automated systems.
- Increased quality care through advanced algorithms and workflows.
We are a fully remote, mission\-driven team united by a shared goal: to reduce cancer’s global impact and help save more of the 10 million lives it claims each year. Every line of code, every product release, and every conversation with our customers brings us closer to ensuring no patient’s treatment quality depends on where they live.
### Why This Role Matters
In this role you will help advance Radformation’s AI\-driven radiotherapy products by building and improving machine learning models that directly impact clinical workflows and patient outcomes.
You will work closely with AI, cloud, research, and product teams to develop scalable data pipelines, improve model performance, and support regulatory submissions for medical device software.
### Responsibilities Include:
- Design, build, and maintain robust ETL pipelines to support AI model development and deployment.
- Develop, train, and optimize machine learning models used in radiotherapy software.
- Collaborate with product and research teams to bring new AI\-driven features and algorithms into production.
- Support FDA submissions by contributing to documentation, validation, and regulatory processes.
- Participate in design reviews, risk analyses, and cross\-functional discussions to ensure safe and effective products.
- Mentor junior engineers and data scientists and contribute to a collaborative team environment.
### Required Experience:
- MS in Computer Science, Mathematics, Statistics, or a related field with 3\+ years of experience.
- Expert\-level proficiency in Python.
- Hands\-on experience building, training, and tuning machine learning models.
- Strong experience with PyTorch and/or TensorFlow.
- Experience developing convolutional neural networks, including U\-Net architectures.
- Experience using Git and modern code repositories (GitHub, Bitbucket, Azure DevOps, etc.).
### Preferred Experience:
- Experience with medical imaging and image processing techniques (segmentation, resampling, smoothing).
- Familiarity with clinical data standards such as DICOM or HL7\.
- Experience working in regulated environments (HIPAA, FDA, or medical device software).
- Experience with modern AI\-assisted development tools (e.g., Cursor, Claude Code, Codex).
### AI \& Hiring Integrity
At Radformation we believe AI can be an incredible tool for innovation, but our hiring process is all about getting to know you, your skills, experience, and unique approach to problem solving. We ask that all interviews and assessments be completed without tools that generate answers in real time. This helps ensure a fair process for everyone and allows us to see your authentic work. Using such tools during the process may affect your candidacy.
### Benefits \& Perks — What Makes Us RAD
We care about our people as much as we care about our mission. We offer competitive compensation, benefits, and the opportunity to make an impact in the fight against cancer. The salary range for this role is $160,000 \- $200,000 USD base, plus bonus eligibility.
For US teammates (via TriNet):
Health \& Wellness
- Multiple high\-quality medical plan options with substantial employer contributions toward premiums, often covering the full cost depending on the plan selected.
- Health coverage starting on day one
- Short\-term and long\-term disability and supplementary life insurance
Financial \& Professional Growth
- 401(k) with employer match vested immediately
- Annual reimbursement for professional memberships
- Conference attendance and continued learning opportunities
Work\-Life Balance \& Perks
- Self\-managed PTO and 10 paid holidays
- Monthly internet stipend
- Company\-issued laptop and one\-time home office setup stipend
- Fully remote work environment with virtual events and yearly retreats, because we like to have fun while doing work that matters
For global teammates (via Deel):
At Radformation, we want every team member to feel supported, no matter where they live. For teammates outside the US, we provide benefits that align with local laws and standards, working with our Employer of Record (EOR) partners to ensure fairness and equity. This means your benefits package will be locally compliant, competitive, and designed to support your health, financial security, and work\-life balance.
### Our Commitment to Diversity
Cancer affects people from every walk of life, and we believe our team should reflect that diversity. Radformation is proud to be an equal opportunity workplace and an affirmative action employer. We welcome candidates from all backgrounds and are committed to fostering an inclusive environment for all employees.
### Agency \& Candidate Safety Notice
Radformation does not accept unsolicited resumes from agencies without a signed agreement in place. We do not partner with third\-party recruiters unless explicitly stated. All legitimate communication from Radformation will come from an @radformation.com email address. If you receive outreach from another domain or via unofficial channels, please contact careers@radformation.com.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $160K-$200K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Radformation, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($180K) sits 8% above the category median. Disclosed range: $160K to $200K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Radformation AI Hiring
Radformation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $200K - $200K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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