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About This Role
We are a well\-funded, late\-stage medical device startup with our sights set on saving lives. We have FDA\-cleared, non\-invasive hardware sensors that acquire patients’ vital signs anytime, anywhere. We have some great products and some great ideas, validated by the US Military, SOCOM, NATO, the Gates Foundation, the Cleveland Clinic, and many others. Our device saved its first life 2 months after our first FDA clearance and we’re now up to seven. I’d love to go into more details here, so we can find the best possible candidate, but we have confidentiality issues to contend with. That will have to wait until we sign an NDA and have a cup of coffee.
We’re looking for a hands\-on engineer to own the development of biosignal processing algorithms and their deployment onto the embedded hardware inside our wearable device. We have a clear technical roadmap, a working platform, firmware infrastructure, and a talented engineering team in place today. We also have real physiological data, validated by government\-funded studies, that will feed the work. Details will have to wait until that virtual cup of coffee, but we need to convert that data and platform into production\-ready, embedded algorithms that run reliably on constrained hardware in austere environments.
You should be intelligent, creative, adaptable, and able to juggle lots of concepts at once. We’re looking for someone whose friends comment that they never forget or lose track of anything, someone who learns things quickly and retains that knowledge. A great attitude goes a long way! You must also be a problem\-solver. You must be ambitious, self\-motivated, and equally able to work independently or in groups. You must also be an effective communicator and able to learn from and work with clients and teammates.
The ideal candidate would have knowledge and experience in wearable biosensor technology and signal processing, with exposure to the Department of War or the federal government as a plus. A bonus would be someone who comes in with thoughts and ideas about managing their tasks, working within project management concepts, and accomplishing goals and objectives.
What we’re really looking for is someone who is smart, ambitious, and can keep track of lots of things at once. You must also be able to learn quickly and apply what you’ve learned; you also need to be able to concentrate on both the forest and the individual trees at different times. Ultimately, we’re looking for aptitude and not decades of experience. We need someone to roll up their sleeves, but this is the kind of role that will most likely morph into a leadership role in the next few years. There may be some travel, but we can locate our trials to your general area over time. \[TT1] \[KS2]
Since we are a startup, everyone has diverse roles and no two days are ever the same. If you like to punch the clock and do the same things every day, this role is probably not for you. We have lots of great resources on staff to learn from. Our company culture is having fun while working hard to save lives.
We are well\-funded with awards and budgets for this work, but the answer cannot be just licensing off\-the\-shelf algorithms. We require creativity to bridge the gap between public\-domain signal processing literature and the constraints of our specific hardware and real\-world use cases. The work must follow best practices and produce defensible, verifiable results, and we will be forward\-thinking in how we approach the engineering tradeoffs.
Confidence is good. I want someone who is confident in their abilities and doesn’t let things get in the way of the positive things they can contribute. These qualities usually lean towards a positive\-leadership style. We believe very strongly in surrounding ourselves with the best and brightest, so should you.
Hopefully, you know who you are and are already planning to respond to this based upon what you’ve read so far. If so, please respond to this posting with your resume so we can get you in the door and on a very promising path.
Responsibilities
1\. Design and implement biosignal processing algorithms for PPG, ECG, body temperature, and inertial sensor data, targeting deployment on ARM Cortex\-M embedded hardware.
2\. Own the full ML development pipeline: feature engineering, model selection and bake\-off, sensor ablation, and quantized model deployment using CMSIS\-DSP and CMSIS\-NN.
3\. Develop and execute data collection protocols in collaboration with the hardware and firmware teams, including multi\-subject labeling and leakage\-safe cross\-validation design.
4\. Apply rigorous statistical methods: whole\-participant holdout, Shapley\-value sensor ablation, pairwise significance testing, and pre\-registered decision thresholds.
5\. Work with MATLAB and Python (NumPy, SciPy, scikit\-learn) for algorithm prototyping, validation, and documentation in support of the design history file.
6\. Collaborate across hardware, firmware, and clinical teams to define sensor integration requirements, timing synchronization constraints, and verification benchmarks for production deployment.
Requirements and Qualifications
- Bachelor’s degree or higher in Electrical Engineering, Biomedical Engineering, Computer Science, Physics, Mathematics, or a closely related field. Graduate or Doctorate degree preferred.
- Hands\-on experience developing signal processing or machine learning algorithms for wearable biosensor data, particularly PPG. Prior work with photoplethysmography is strongly preferred.
- Proficiency in MATLAB and Python (NumPy, SciPy, scikit\-learn). Experience implementing and benchmarking algorithms in both environments is required.
- Demonstrated experience with embedded ML deployment on ARM Cortex\-M processors (nRF52840 or similar), including quantization, CMSIS\-DSP / CMSIS\-NN integration, and hardware\-in\-the\-loop profiling.
- Functional knowledge of statistical analyses and reporting.
- High native intelligence: fast learner, sharp pattern recognition, retains detail without being reminded.
- Genuine creativity: comfortable finding compliant, defensible ways to execute unconventional testing scenarios, not just the *standard playbook.*
- Working knowledge of wearable biosensor technology: PPG contact mechanics, ECG signal quality, IMU\-based motion artifact characterization, 3D spatial optimization, and multi\-sensor fusion.
- Algorithm development experience is a significant plus: rule\-based, classical ML (logistic regression, SVM, tree ensembles), and small neural models for tabular biosignal features.
- Exceptional communication and interpersonal skills: genuinely easy to work with, low\-ego, and builds trust quickly with teammates, clients, and government partners.
- Strong self\-motivation: productive and disciplined working from home without oversight.
- Excellent organization, memory, and attention to detail: nothing falls through the cracks.
- Time management and the ability to juggle multiple concurrent workstreams.
- Problem\-solving orientation, focused on business objectives over process for its own sake.
- Office 365, SharePoint, and Git proficiency.
- Professional experience using AI\-assisted research and development tools and a clear understanding of their appropriate applications and limitations.
\*\* No offshore resources or hiring agents need respond. You would predominantly work remotely with few trips per year. The preferred location is Austin, TX or San Antonio, TX (proximity to our lab and partner facilities is a plus). Other considerations may be given to individuals located in Texas, Florida, Georgia, Idaho, Pennsylvania, Maryland, Delaware, Virginia, or California.
Job Type: Full\-time
Benefits:
- 401(k)
- Health insurance
- Paid time off
Application Question(s):
- Briefly describe your experience with signal processing.
- Briefly describe a project where you had to analyze PPG data.
- Briefly describe how you would design a validation study for a biosignal classification algorithm where you have data from 15 subjects. How would you split the data, and why does it matter?
Work Location: Remote
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 Confidential Startup, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
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.
Confidential Startup AI Hiring
Confidential Startup has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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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