Interested in this AI/ML Engineer role at nLIGHT, Inc.?
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About This Role
Experience: 8+ years of professional software, firmware, or data engineering experience shipping and maintaining production codeEducation: B.S. in Engineering, Science, or related Field. Location: Longmont, CO (Onsite)Compensation: $145,000-$200,000 depending upon education, skills, and experience.Who We Are
nLIGHT-DEFENSE Systems, Inc. is one of the nation’s leading developers of Directed Energy systems, which is a top modernization priority of the Department of Defense. Our High Energy Laser (HEL) systems, Adaptive Optical (AO) systems, and Acquisition / Pointing / Tracking (APT) solutions are designed to overcome the most difficult challenges facing the deployment of high energy laser solutions to the United States military and our allies. Located in Longmont, Colorado, nLIGHT-DEFENSE Systems, Inc. is a vertically integrated business that leverages internal capabilities – from semiconductor device through target identification – to develop systems that are both high-performance and cost-effective. We continue to invest in capabilities, facilities and technology to bring leading edge HEL solutions to the warfighter. This is a great opportunity to work at a company that is on the leading edge of the HEL industry and provides a culture of excellence and commitment to delivering solutions to the warfighter and our country.
Headquartered in Camas, Washington, nLIGHT is a publicly listed company (NASDAQ: LASR).
Summary:
The AI Integration Engineer is responsible for evaluating, deploying, and advancing AI technologies that improve engineering productivity, quality, and innovation across nLIGHT. This role partners closely with software, firmware, and engineering teams to integrate AI-enabled tools, automation, and workflows into development environments while ensuring compliance with CUI, ITAR, and company security requirements. The position also serves as a technical advisor and educator, helping teams effectively adopt and leverage emerging AI capabilities. Success is measured by the sustainable adoption of AI solutions that drive measurable improvements in engineering efficiency, effectiveness, and business outcomes.
All applicants must possess or be qualified to obtain a U.S. DoD Personnel Security Clearance. Preference will be given to candidates with an existing U.S. DoD Personnel Security Clearance. Further information on requirements to obtain a security clearance is available at: https://www.dcsa.mil/mc/pv/mbi/gicp/. Please review this information before applying.
Responsibilities:
- Evaluate, implement, and optimize AI technologies, tools, and workflows that improve engineering productivity, quality, and effectiveness across the organization.
- Assess, deploy, and support AI-enabled development tools, agentic coding solutions, and automation capabilities within engineering environments and CI/CD pipelines.
- Design, develop, and maintain AI-powered applications, integrations, and workflows utilizing large language models (LLMs) and related technologies where commercially available solutions do not meet business or technical requirements.
- Identify and implement opportunities to leverage AI-assisted automation across software and firmware development, testing, code review, documentation, requirements traceability, quality assurance, and other engineering processes.
- Partner with engineers, technical leaders, and cross-functional stakeholders to drive adoption of AI technologies through training, coaching, workshops, best practices, and technical guidance.
- Establish and maintain standards, governance, and review processes for the responsible, effective, and secure use of AI-assisted development tools and AI-generated content.
- Collaborate with IT, Security, and business stakeholders to ensure AI tools, workflows, and data usage comply with company policies, cybersecurity requirements, export controls, Controlled Unclassified Information (CUI) handling requirements, and International Traffic in Arms Regulations (ITAR).
Required Experience \ Capability:
- Commitment to Excellence. Attention to detail. Pride in workmanship.
- 8+ years of experience in software, firmware, or data engineering with production systems.
- Demonstrated experience building and deploying solutions using LLM APIs or AI-assisted development tools.
- Proficiency in C#/.NET, C++, or Python, with the ability to work across multiple programming languages.
- Experience integrating and supporting external APIs and services.
- Hands-on experience with agentic coding tools and AI-assisted software development workflows.
- Proficiency with Git and CI/CD pipelines; GitLab experience preferred.
- Strong technical communication, training, and knowledge-sharing skills.
- Sound judgment with the ability to evaluate where AI solutions add value and where they do not.
- Ability to work independently, manage ambiguity, and learn unfamiliar technologies quickly.
Desired Experiences:
- Experience in aerospace, defense, or another regulated industry.
- Familiarity with ITAR, CUI, NIST SP 800-171, or CMMC.
- Experience with Azure GCC High, AWS GovCloud, or other government cloud environments.
- Experience deploying and serving models on-premises: GPU infrastructure, inference servers, quantization, throughput and latency tuning.
- Experience with retrieval-augmented generation, evaluation harnesses, or Model Context Protocol (MCP) server development.
- Background in embedded, real-time, or hardware-in-the-loop software.
- Experience running an internal training, enablement, or developer experience program.
- Familiarity with electro-optical systems, Adaptive Optics, or High Energy Laser systems.
Benefits Summary:
- 4 weeks of Paid Time Off per year
- 11 paid Holidays
- Employee Stock Purchase Plan
- Tuition Assistance Program
- Health (ACA Gold Plan), Vision and Dental Care paid by employer 100% for
Employee and 75% for Dependents* Paid Family Leave, Short and Long Term Disability paid by employer 100%
- Competitive 401k with company match and immediate vesting
Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or age. For more information about our commitment to equal employment opportunity, please see this government poster: Know Your Rights: Workplace Discrimination is Illegal. If you are an individual with a disability and need a reasonable accommodation in the application or hiring process, please contact Human Resources at [email protected].
Salary Context
This $145K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At nLIGHT, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($172K) sits 19% below the category median. Disclosed range: $145K to $200K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
nLIGHT, Inc. AI Hiring
nLIGHT, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Longmont, CO, US. Compensation range: $200K - $200K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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