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About This Role
Philadelphia, PA (Hybrid)
Reports to: Director of Design
About Proscia
Proscia is revolutionizing pathology, the last major frontier in healthcare to embrace digital. As a leader in pathology AI software, we are empowering pathologists and scientists to transition from traditional microscope\-based workflows to digital, AI\-driven approaches, unlocking new possibilities in precision medicine.
The digital pathology market is experiencing explosive growth as advances in AI enable unprecedented insights into diseases like cancer. Pathology is central to medicine, and the shift to AI\-powered solutions is not just modernizing workflows—it’s transforming how diseases are diagnosed, treated, and understood. Predictions for the future of pathology show a tidal wave of adoption, with experts describing the field as “poised for the next major breakthrough” in healthcare innovation.
Backed by over $100 million in funding from leading healthcare and technology investors, Proscia is at the forefront of this revolution. Joining Proscia means being part of a company at the cutting edge of healthcare innovation, where the possibilities are limitless. With the convergence of AI, precision medicine, and digital pathology, we’re not just changing pathology—we’re redefining what’s possible in medicine.
About This Position
We're hiring a Product Designer to own discrete product areas end\-to\-end, working directly with our Director of Design. This is a product\-first role: the large majority of your time will go to product craft, from problem framing through shipped, validated experience. You'll also bring a visual design sensibility beyond typical UI work, a sharper eye for iconography, illustration, and data visualization styling, that shows up inside the product and, occasionally, in a supporting brand or marketing asset.
Experience designing regulated medical device products is a plus. Experience designing AI or ML\-driven product experiences is required, see more below.
What You'll Do
- Own discrete product areas end\-to\-end, from problem framing through shipped, validated experience, including the design of our AI\-powered intelligence layers (model outputs, confidence scoring, human\-in\-the\-loop review, explainability).
- Design for human\-AI collaboration: how pathologists interpret, trust, override, and act on AI\-generated insights, and how we surface uncertainty without eroding clinical confidence.
- Lead research efforts to understand our users, pathologists and scientists, through observation, interviews, and usability testing, partnering with the Director of Design on structure and direction.
- Own empathy maps, user personas, and journey maps that keep the user at the center of what we build.
- Use visual hierarchy, layout, and information architecture skills to design storyboards, process flows, and sitemaps; create UI mockups, wireframes, and high\-fidelity designs.
- Generate graphical elements, icon sets, illustration, onboarding graphics, data visualization styling, and contribute to the Proscia design system.
- Collaborate with cross\-functional teams, including product managers, engineers, and scientists, to translate user insights and AI/ML capabilities into functional, elegant design solutions.
- Design with awareness of compliance frameworks for regulated medical device software (e.g., IEC 62366, FDA guidance for human factors engineering), and participate in human factors and usability testing as needed.
- Look for ways to infuse AI into the design process, improving user experiences, workflows, speed of execution, and product quality.
- Occasionally support marketing and sales with polished visuals for decks, one\-pagers, websites, or event materials, using existing brand guidelines. This is light, occasional support with guidance from design leadership, not an ownership responsibility.
What We're Seeking
This role sits between product and visual craft, and now, increasingly, at the center of how pathologists interact with AI. We're looking for someone with the range to do all three well.
- 3 to 5 years of experience as a Product or UI/UX Designer
- Real experience designing AI or ML\-driven product experiences: surfacing model confidence, explaining outputs, supporting human\-in\-the\-loop review, and calibrating user trust in automated systems. This is a requirement, not a nice\-to\-have.
- Demonstrated proficiency in design software such as Figma
- A portfolio showing strong end\-to\-end product craft, plus a visual design sensibility beyond standard UI work: typography, layout, and illustration or iconography instincts
- Prior experience or coursework in graphic design, illustration, or visual/brand design
- Experience designing web applications, ideally built for professional use
- Ownership of design system contributions, including maintaining and scaling shared component libraries
- Strong communication skills, with the ability to collaborate across disciplines and explain design decisions clearly, including to non\-technical stakeholders
- Basic understanding of front\-end technologies (HTML, CSS, JavaScript)
A few additional qualifications would be a plus, though they're not required:
- Experience with regulated medical device software, including familiarity with human factors engineering standards (e.g., IEC 62366\) and FDA usability guidance
- Knowledge of scientific, healthcare, or laboratory workflows
- Comfort in Illustrator or Photoshop for occasional supporting assets
Beyond Just Work
As a company in healthcare, we want our people to be happy and healthy, in and out of the office. In addition to competitive pay, we ensure everyone on our team is supported with savings, schedule, and insurance options that promote long\-term health and personal growth.
Our office environment is designed for creativity and agility: with walls as notepads and couches for collaboration. We’re located in the heart of Philadelphia, with views of the city so you can spend your time focusing on what matters most.
At Proscia, we don’t just accept differences — we celebrate them, we support them, and we thrive on them for the benefit of our employees, our products, and our community. Proscia is proud to be an equal opportunity workplace.
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 Proscia, 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.
Proscia AI Hiring
Proscia has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Philadelphia, PA, US.
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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