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About This Role
The Air Traffic Control & Weather Systems Group at MIT Lincoln Laboratory has a long history of developing innovative and impactful technologies that enable safe, efficient, and secure transportation. We work in a wide range of mission areas, including weather sensing and forecasting, air traffic control decision support and aviation cyber security. Our group brings together experts across multiple disciplines – systems analysis, meteorology, mathematics, computer science, aerospace engineering, human factors, flight operations, and others – to address the nation’s transportation problems. We develop advanced prototype systems and work closely with the US government, users, industry and academia to integrate and transfer them into real-world operations. Our technologies have been recognized nationally through numerous R&D100 awards, and we are proud to serve the country through our research.
Job Description
We are seeking an AI/ML engineer to develop applications in weather and Earth systems domains from end to end, including dataset procurement, algorithm development and training, and integration of models into operational prototypes. A solid foundation in data science is critical for this role, as is a deep technical knowledge of weather and/or Earth systems science. Experience applying modern machine learning techniques to satellite and radar observations, in situ meteorological measurements, camera, satellite imagery, or numerical weather prediction models is strongly desired. The ideal candidate will be able to think broadly about how modern AI/ML approaches can enhance environmental sensing, forecasting, and decision support capabilities, and be eager to contribute to both real-time operational system development and research-oriented trade studies.
Key project opportunities include:
- Conceptualizing, developing, testing, and deploying AI/ML algorithms using large‑scale environmental datasets and high‑performance computing resources
- Assessing algorithm performance through rigorous statistical and operational metrics, and documenting results in formal publications
- Building novel models and decision-support tools for weather situational awareness
- Contributing to NOAA, FAA, and DoW programs aimed at advancing national weather sensing, forecasting, and decision support needs
Our team provides both high-level and deeply technical analyses, including trade studies, reports, presentations, and publications (conferences, journals, etc.) for government sponsors. We take pride in serving as independent subject matter experts to the U.S. government and seek individuals who value the responsibility and impact associated with this mission.
Requirements
- Ph.D. in Meteorology, Earth Systems Science, or related field of study, or M.S. in a related field of study with 5+ years of relevant work experience
- Demonstrated proficiency in Python and Linux environments
- Experience with advanced AI/ML techniques, specifically deep learning approaches
- Strong foundation in meteorological datasets and/or Earth system science
- Experience with high-performance supercomputing and/or cloud computing environments
- Strong analytical skills and demonstrated experience with very large datasets
- Demonstrated proficiency in technical writing through journal publications, conference papers, and/or reports
- Outstanding oral presentation skills as demonstrated by conference presentations, etc.
Recent Graduate Hiring Range: $145,200 - $170,000
Experienced Hiring Range: $145,200 - $220,000
Disclaimer: MIT Lincoln Laboratory provides a typical hiring range as a good faith estimate of what we reasonably expect to offer for this position at the time of posting. The final salary offered to a selected candidate will depend on various factors, including—but not limited to—the scope and responsibilities of the role, the candidate’s experience, skills and education/training, internal equity considerations and applicable legal requirements. This range reflects base salary only and does not include additional forms of compensation or benefits.
At MIT Lincoln Laboratory, our exceptional career opportunities include many outstanding benefits to help you stay healthy, feel supported, and enjoy a fulfilling work-life balance. Benefits offered to employees include:
- Comprehensive health, dental, and vision plans
- MIT-funded pension
- Matching 401K
- Paid leave (including vacation, sick, parental, military, etc.)
- Tuition reimbursement and continuing education programs
- Mentorship programs
- A range of work-life balance options
- ... and much more!
Please visit our Benefits page for more information. As an employee of MIT, you can also take advantage of other voluntary benefits, discounts and perks.
Selected candidate will be subject to a pre-employment background investigation and must be able to obtain and maintain a Secret level DoD security clearance.
MIT Lincoln Laboratory is an Equal Employment Opportunity (EEO) employer. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, veteran status, disability status, or genetic information; U.S. citizenship is required.
Requisition ID: 43418
Salary Context
This $145K-$220K range is above 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 MIT Lincoln Laboratory, 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 ($182K) sits 14% below the category median. Disclosed range: $145K to $220K.
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.
MIT Lincoln Laboratory AI Hiring
MIT Lincoln Laboratory has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Lexington, MA, US, Lexington, MA. Compensation range: $66K - $220K.
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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