Interested in this AI/ML Engineer role at Epitel, Inc.?
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About This Role
Position Summary
The Machine Learning Scientist uses scientific, mathematical, and computational (software) skills to advance research and clinical (commercial) products for Epitel. This role is heavily involved with all machine learning and data science aspects of the company. This role is also involved with research and grant\-funded aims.
Key Responsibilities
- Research, develop, and implement modern computational techniques for time\-series analysis, including classical and neural network machine learning methods.
- Develop and maintain scientific codebases in Python including data ingestion, processing, machine learning, post\-processing, evaluation, and visualization.
- Develop performant scientific software utilizing parallelism and capable of operating in distributed computing environments.
- Engineer data workflows for ML experiments that track data/feature/experiment versions with transparency.
- Report key findings in academic journals and at conferences.
Minimum Required Qualifications
- Education: Master’s degree in a computational science field (e.g., Computer Science, Machine Learning, Statistics, Computational Neuroscience)
- Programming: Must be proficiently skilled in Python based scientific programming and machine learning \- e.g., Scikit\-Learn, Numpy, Pandas, SciPy, Tensorflow, Keras, PyTorch, etc.
- Mathematics: Must be strong with foundational mathematics and statistics
- Machine Learning Techniques: Proficiency in modern ML architectures, unsupervised learning techniques, data modeling, ML OPs utilities.
- Experience: 4\+ years relevant industry or related experience working specifically on ML development for time\-series applications.
- Other: Must be able to travel to and participate in in\-person scientific conferences
Preferred Qualifications
- Strong preference for a candidate with experience in developing ML workflows for EEG analysis specifically or other neuro/bio/physiological time\-series data.
- Background in scientific software engineering and/or High\-Performance Computing
- Experience working with TB\-scale disaggregated datasets
- AWS / Data engineering experience
- Publication experience
Key Competencies
- Divergent Thinking: the ability to come up with novel solutions to open\-ended problems or identify new questions/new problems
- An understanding of software design principles, including the ability to write flexible, interpretable code for use by other teammates
- Minimal Oversight \- We are a small and growing company and this role will require one to work diligently without constant oversight. Open communication is critical.
- QMS \- Must be able to document all aspects of the code\-base and deployment in a clear and well\-detailed manner.
Company \& Culture
Epitel is a venture\-backed company empowering clinicians, patients, and caregivers who live with or care for neurological disorders. Epitel employs proprietary EEG wearables and powerful ML/AI to create a platform for brain diagnostics. Our system cuts the time to diagnose brain disorders from hours to minutes and solves critical care access issues. We’re building toward a continuous neurology care platform that will create novel biomarkers for neurological disorders. If you’re interested in a high\-impact role at a neurology pioneer, this is it!
Benefits
- Remote\-friendly \- we’re open to the best candidate anywhere in the US. To that end, we provide a stipend for a remote worksite of your choice (e.g., WeWork, Industrious) and WFH gear (e.g., keyboard, mouse, camera, etc.)
- Equity \- every employee shares in Epitel’s success
- Professional development allowance \- we do better when you do better
- Medical/dental coverage \- we pay 100% of premiums
- Simple IRA contribution
- Unlimited vacation \- we also take typical US holidays
Interested Applicants Can Apply at: https://forms.gle/mafrArsW6PhGpqGB7
Pay: $130,000\.00 \- $180,000\.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Flexible schedule
- Health insurance
- Health savings account
- Paid time off
- Parental leave
- Retirement plan
- Vision insurance
People with a criminal record are encouraged to apply
Experience:
- time\-series machine learning development: 3 years (Required)
Work Location: Remote
Salary Context
This $130K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Epitel, Inc., 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($155K) sits 29% below the category median. Disclosed range: $130K to $180K.
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.
Epitel, Inc. AI Hiring
Epitel, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $180K - $180K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 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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