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About This Role
Overview:
Lynker is seeking a talented and experienced Machine Learning Engineer to support the Environmental Modeling Center (EMC) within the National Centers for Environmental Prediction (NCEP). The primary objective of this role is to assist in the development of ML based systems that predict the current weather conditions everywhere given sparse observation data (this process is known as Data Assimilation \[DA]). . These systems will complement existing physics\-based systems and be tested as independent prototypes, running alongside traditional DA workflows. The position is located at the NOAA Center for Weather and Climate Prediction (NCWCP) in College Park, MD.
Responsibilities:
Duties of the Machine Learning Engineer will include the following:
The Machine Learning Engineer will perform their job duties to a high standard, working both independently and collaboratively.The core responsibility is to assist in the development, implementation, testing, and evaluation of an AI\-based Real\-Time Mesoscale Analysis (AI\-RTMA) system in support of NOAA’s National Blend of Models (NBM). The AI\-RTMA system will generate high spatial and temporal resolution analyses of meteorological variables to reduce biases in the NBM fields.. Because these fields serve as the foundation for gridded forecasts issued by the National Weather Service, this system will directly contribute to improved forecast quality.
The successful Machine Learning Engineer will work on the following scientific and engineering tasks:
- Conduct a comprehensive review of state\-of\-the\-art AI\-based data assimilation and end\-to\-end weather forecasting methodologies, systems, and frameworks. Communicate findings with EMC scientists and external partners to inform the development of a scientifically robust and efficient AI\-RTMA approach.
- Collaborate with NOAA’s NBM team and key stakeholders to define product requirements for AI\-RTMA, including domain configuration, grid structure, output variables, spatial and temporal resolution, and data formats suitable for operational evaluation and transition.
- Design, implement, and maintain robust data pipelines to support AI\-RTMA training, validation, testing, and evaluation. This includes collecting, formatting, quality\-controlling, and integrating diverse observational datasets (e.g., conventional observations, satellite, radar, and other sources), as well as preparing model inputs, targets, metadata, and training/validation splits.
- Develop, train, rigorously test, and deploy a fully functional AI\-RTMA system based on selected AI frameworks or architectures.
- Implement cross\-validation and other evaluation methodologies to quantify model performance and reliability during inference.
Qualifications:
The Machine Learning Engineer selected should have the following:
- Experience developing, training and deploying AI\-based systems applied to geophysical systems.
- Experience with common AI frameworks such as PyTorch, TensorFlow.
- Experience working with earth observation data, including conventional observations, satellite, radar.
- Excellent Python programming skills.
- Practical experience utilizing High Performance Computers (HPCs) and GPUs.
- Proven experience working in a UNIX environment with advanced scripting languages.
- Good communication skills, both oral and written, in English.
The Ideal Machine Learning Engineer will have the following:
- In\-depth knowledge of data assimilation techniques (observation forward modeling, quality control, variational\-based and/or ensemble methods).
- Strong foundation in the physical, statistical and mathematical basis of geophysical modeling (atmospheric and/or environmental).
- Experience with cloud platforms and use of IDEs for development.
- Experience with cloud\-native data formats such as Zarr, Parquet.
- Experience with compiled languages.
- Comfort using agentic AI tools to accelerate development.
- Experience executing numerical models on HPC platforms using parallelization frameworks and job scheduling systems.
- Familiarity with coupled earth system models.
- Knowledge of modern software engineering practices (requirements gathering, design, prototyping, version control, integration, testing, and documentation).
- Prior experience in model testing, evaluation, or knowledge of verification principles.
About Lynker
Lynker is a growing, employee owned business, specializing in professional, scientific and technical services. Our continually expanding team combines scientific expertise with mature, results\-driven processes and tools to achieve technically sound, cost effective solutions in hydrology/water sciences, geospatial analysis, information technology, resource management, conservation, and management and business process improvement.
We focus on putting the right people in the right place to be effective. And having the right people is critical for success. Our streamlined organization enables and empowers our talented professionals to tackle our customers' scientific and technical priorities – creatively and effectively.
Lynker offers a team\-oriented work environment, and the opportunity to work in a culture of exceptionally skilled professionals who embrace sound science and creative solutions. Lynker's benefits include the following:* Comprehensive healthcare for the employee at no monthly cost
- Healthcare benefit covers medical, prescription drug, dental, and vision
- Personal Time Off (PTO) Policy plus paid holidays
- Highly competitive compensation plan regularly calibrated against industry and location benchmarks
- 401(k) retirement plan with company\-matching
- Employee Stock Ownership Plan (ESOP) – we're all company owners!
- Flexible spending accounts
- Employee assistance program (EAP)
- Short\- and long\-term disability insurance
- Life and accident insurance
- Tuition assistance/Training/Workforce improvement reimbursement per year
- Spot bonuses for exceptional performance
- Annual Employee Recognition Awards with bonuses
- Employee Referral Program
- Free centralized, self\-directed Learning Management System to learn at your own pace
- Personalized career growth plans for every employee
Lynker is an E\-Verify employer. Lynker is an equal opportunity employer and makes all employment decisions based on merit, qualifications, and business needs. We do not discriminate on the basis of race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, marital status, veteran status, or any other legally protected status under federal, state, or local laws.* ️ Fraud Alert: Recruitment Scam Warning: Lynker has been made aware of fraudulent individuals posing as Lynker recruiters and offering fake job opportunities. All legitimate Lynker job postings are listed on our official careers page. Communication from Lynker recruiters will come from an official @lynker.com email address.
Salary Context
This $95K-$195K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Lynker, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($145K) sits 34% below the category median. Disclosed range: $95K to $195K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Lynker AI Hiring
Lynker has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span College Park, MD, US, Seattle, WA, US. Compensation range: $125K - $195K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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