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About This Role
Optics and Computer Vision (Systems) Engineer
Sciton is a Silicon Valley based pioneer in laser, light, and energy technologies for aesthetic and medical applications. Our engineers brought laser products to market just a few years after lasers were first invented, and that same spirit of innovation still drives us today.
We are built on a strong set of values: Selflessness, Candor, Innovation, Execution, Objectivity, Excellence, Learning, Ownership, and Clarity. With more than 500 employees worldwide, Sciton has been recognized as a USA TODAY Top Workplace for four consecutive years from 2022 to 2025\.
If you want to work on meaningful technology that directly improves patient lives and be part of a company that values curiosity, ownership, and excellence, we would love to meet you.
Position Summary
As an Optics and Computer Vision (Systems) Engineer, you’ll play a key role in advancing Sciton’s next generation of medical and aesthetic products. You’ll be embedded within both the R\&D and software teams, helping to bring new vision\-based technologies from concept to production.
Your primary focus will be developing imaging and computer vision systems that analyze skin lesions for subsequent control of peripheral devices and actuators. These systems directly support cutting\-edge treatments used by clinicians and benefit patients worldwide.
You’ll collaborate closely with software, hardware, and clinical teams to guide projects through Sciton’s New Product Introduction (NPI) process—turning innovative ideas into real\-world products.
What You’ll Work On
- Develop in a mixed hardware and software environment optical systems and real\-time computer vision algorithms for medical skin imaging and skin treatment actuator control
- Select camera and illumination hardware resulting in high image/video quality and consistent
- skin lesion assessment under varying light conditions
- Develop robust algorithms for:
+ Multi\-sensor and multi\-camera image fusion
+ Image registration, segmentation and feature extraction
+ Detection and quantitative assessment of visual events
+ Object motion compensation
+ Optimized visual servoing
- Integrate vision processing to control peripheral devices and actuators, optimized for low latency and high reliability
- Spearhead optical calibration, illumination design, image quality analysis, and system\-level troubleshooting along the various technology pipelines
+ Resolve image distortion errors and alignment failures by systematically analyzing interactions across imaging pipeline subsystems.
+ Devise end\-to\-end calibration workflows, including target selection, data acquisition procedures, automated calibration execution and output of quantitative calibration quality metrics
- Evaluate and adapt existing visual servoing and vision algorithms for clinical use
- Work with external technology partners and vendors to select and refine imaging solutions
- Contribute to patent filings and support regulatory documentation, including risk analysis and system validation
- Support the development of ML and AI powered technologies beneficial to users
What We’re Looking For
- Strong experience delivering high\-quality optical images for users and sub\-systems
- Hands\-on, systems\-oriented experience creating and refining algorithms for medical skin
- imaging and treatment control, including:
+ Image registration, segmentation, feature extraction, object/defect detection, coordinate transformation and stereo depth determination
+ Multi\-sensor and multi\-camera image fusion
+ Real\-time guidance of peripheral treatment devices with low latency
+ Visual servoing \& motion compensation
- Strong programming skills in C/C\+\+ and Python, with solid object\-oriented design experience
- Knowledge of camera pipelines, 2D/3D vision algorithms, sensors, displays, and illumination systems
- Strong understanding of image, video, illumination and visual servoing quality metrics and how to apply them for performance verifications
- Experience with embedded Linux and platforms such as ARM / NXP i.MX, plus tools like Gstreamer, PyTorch, TensorFlow, OpenCV, Zemax and other toolkits
Core Qualifications
- Master’s or PhD in Optical Physics, Computer Science, Biomedical Engineering, or a related field
- Minimum of 4 years of relevant industry experience
- Strong theoretical and practical background in optical imaging, computer vision, image/video
- processing, actuator controls for low latency and related performance verification metrics
- Experience working with real\-time systems, control loops, and camera communication protocols (USB, MIPI, etc.)
- Proven ability to take projects from concept through execution and delivery
- A collaborative mindset and strong problem\-solving skills
- Preferred Skills \& Experience
- GPU/CUDA acceleration
- FPGA, NPU, or hardware\-accelerated vision pipelines
- Memory\-mapped I/O, DMA, and real\-time performance optimization
- Vision pipeline latency reduction and throughput optimization on edge devices
- Experience in 3D imaging
- Experience in biological imaging, quantitative contrast analysis and tissue perfusion assessment
- Experience in denoising, deblurring, color processing, pixel‑level correction, lens shading and distortion correction, quantitative image‑quality assessment (MTF, SNR, contrast).
- MIPI CSI camera integration, ISP tuning, and sensor control (I2C, SPI)
- H.264/H.265 video encoding for live video streams
- Thermal management, power optimization, and EMC considerations
- ROS/ROS2 experience (if vision interacts with robotic systems)
Compensation and Benefits
The salary range for this position is $150k \- $220k. In addition to a competitive market\-based salary, Sciton provides an opportunity to participate in equity/stock incentive programs, a profit\-sharing bonus, and a comprehensive benefits package, including 401(K) with matching
FULL\-TIME/PART\-TIME Full\-Time
POSITION Optics and Computer Vision (Systems) Engineer
LOCATION Palo Alto, HQ
Salary Context
This $150K-$220K range is above the median 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 Sciton 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($185K) sits 14% below the category median. Disclosed range: $150K to $220K.
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.
Sciton Inc. AI Hiring
Sciton Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Palo Alto, CA, US. Compensation range: $220K - $220K.
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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