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About This Role
Janicki Industries is an innovative, family\-owned aerospace company located at the foot of the Cascades. We focus on engineering and manufacturing complex projects for companies in the aerospace, defense, and space industries. We are looking for a Sr. AI Robotics Engineer to join our growing team.
POSITION DESCRIPTION
This position is located on\-site in Bellingham, Washington State.
Join a new, high\-impact Robotics \& AI team chartered with deploying next\-generation intelligent automation. You will design, develop, integrate, and continuously improve AI\-enhanced robotic systems (manipulators, mobile platforms, cobots) that perform manufacturing tasks and support with increasing levels of autonomy.
This is a hands\-on individual contributor role with immediate leadership/mentoring responsibilities. You’ll work side\-by\-side with mechanical, electrical, and manufacturing engineers in a fast\-moving production environment where prototypes go live on the shop floor in days or weeks—not months.
The following essential job functions are performed as an Sr. AI Robotics Engineer:
- Architect and implement perception systems (2D/3D vision, point clouds, force/torque) using modern AI frameworks
- Train, deploy, and iterate on object\-detection, segmentation, pose estimation, and anomaly\-detection models in real manufacturing settings
- Apply reinforcement learning and other advanced techniques to improve robot dexterity, path planning, and error recovery
- Develop on\-edge AI solutions using NVIDIA Jetson platforms (Orin, Xavier, etc.)
- Integrate industrial robots (Fanuc, ABB, KUKA, Universal Robots, etc.) with vision and AI pipelines
- Write robust production\-grade Python code, manage repositories in Git, and containerize solutions with Docker
- Stand up and maintain development/workstation environments (Linux/Windows, ROS/ROS2, CUDA, virtual machines, etc.)
- Mentor junior engineers and collaborate cross\-functionally with ME, EE, and manufacturing teams
- Rapidly prototype, validate on the floor, and transition solutions to sustained operations
- Other duties as assigned
REQUIRED QUALIFICATIONS
- 10\+ years of relevant engineering experience spanning robotics and AI required.
- Bachelor’s degree in Mechanical Engineering, Computer Science, or closely related field from an ABET\-accredited, competitive university
- Deep hands\-on experience with industrial robot programming, integration, and deployment
- Proven track record shipping applied AI/ML solutions (object detection, model training/re\-training loops, reinforcement learning, etc.) in real\-world environments
- Strong background in robot perception (cameras, LiDAR, structured light, force sensing)
- Extensive NVIDIA Jetson development experience (JetPack, TensorRT, DeepStream, Isaac ROS, etc.)
- Expert\-level Python, common AI ecosystems (PyTorch/TensorFlow, OpenCV, PCL, ROS/ROS2\), and modern dev\-ops tooling (Git, Docker, CI/CD)
- Comfortable installing, configuring, and troubleshooting Linux/Windows systems, VMs, and complex development environments
- Demonstrated ability to mentor engineers and lead technically without formal authority
- Thrives in cross\-disciplinary hardware \+ software environments
- Bias for action, fast pace, and getting results with minimal oversight
- Self\-starter who can define, prioritize, and execute projects end\-to\-end
- Due to our ITAR and EAR regulations, applicants must be a US Citizen or of Legal Permanent Resident Status as defined by 8 U.S.C. 1324b (a) (3\)
- This position requires the ability to obtain a U.S. Secret Security Clearance (U.S. Citizenship Required). Janicki will assist with gaining this access once employed. Special Access Program or other Government Access Requirements are mandatory for this position and requires candidate agreed to enter a Continuous Evaluation program
PREFERRED QUALIFICATIONS
- MS or PhD in Robotics, AI, or related field preferred
- Experience in aerospace or similarly regulated manufacturing preferred
- Previous work with Isaac Sim, MoveIt, or other advanced robotics frameworks preferred
ADDITIONAL INFORMATION
- Salary range for this role is between $125,000 \- $200,000, plus discretionary bonus, 401(k) matching, vacation, and health benefits. Employees can also receive additional pay for off shifts. The range provided is Janicki’s estimate of the base compensation for this role. Actual amount offered will be based on job\-related and non\-discriminatory factors such as experience, location, education, training, skills, and abilities
- By applying for this job, you are expressing interest in this position and could be considered for other career opportunities where similar skills and requirements have been identified as a match. Should this match be identified, you may be contacted for this and future openings
BENEFITS
- Medical, dental, and vision insurance with employer contribution
- Disability insurance as well as Life/AD\&D insurance
- HSA (Health Savings Account) with employer contribution and FSA (Flexible Savings Account)
- 401k with employer matching
- Paid time off and paid holidays (including two floating holidays)
- Education reimbursement program
- Several shift options
- Premium pay for off shifts
*Not sure that you’ll be the perfect fit for this role? You should still apply! We’ll review your application for other opportunities. We are always on the lookout for talented people!*
*Janicki Industries is an Equal Opportunity Employer. Janicki Industries does not discriminate on the basis of race, color, religion, sex, national origin, sexual orientation, marital or familial status, physical or mental disability, genetic information, age, retaliation, veteran/military service status, or any other legally protected status. Janicki is proud to be a military friendly employer.*
*Applicants or employees wishing to view a copy of Janicki Industries’ Affirmative Action Plan for veterans and individuals with disabilities, or applicants requiring reasonable accommodation to the application/interview process should notify the Human Resources Department at (360\) 404\-1997\.*
*As a federal government contractor and a recipient of federal funding, Janicki is required to abide by federal drug testing requirements (including preemployment drug testing for cannabis). Additionally, because of Janicki’s work on aerospace products and the high volume of safety sensitive positions, Janicki takes the safety of its employees very seriously and requires that employees pass a preemployment drug test prior to starting employment.*
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Salary Context
This $125K-$200K range is below 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 Janicki Industries, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($162K) sits 24% below the category median. Disclosed range: $125K to $200K.
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.
Janicki Industries AI Hiring
Janicki Industries has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Bellingham, WA, US. Compensation range: $200K - $325K.
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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