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About This Role
The Secure Resilient Systems and Technology group advances cybersecurity research to address critical national security challenges. We develop and evaluate technologies that enable mission systems, from space and autonomous platforms to tactical communications and secure computing, to withstand cyberattack and continue the mission. Our goal is not simply to identify vulnerabilities, but to design security in from the start and provide rigorous evidence that these systems can be trusted under attack.
Our research spans secure software and embedded systems, security architecture and threat modeling, applied cryptography, formal methods, and security evaluation, as well as the intersection of AI and cybersecurity, including both AI for cybersecurity and cybersecurity for AI. We work across the R&D lifecycle, from new concepts through prototyping and transition to mission use, collaborating across Lincoln Laboratory and with government, industry, and academia.
We are a collegial and mission-driven team working on difficult problems where security, resilience, assurance, performance, and operational realities all matter. We value technical excellence, innovation, collaboration, inclusion, and individual growth. Our culture emphasizes mentoring and developing researchers into PIs and technical leaders. We strive to bring out the best in our people and our systems, so that the Laboratory and the nation can count on them when it matters most.
If you would like to pursue cutting-edge research and work on things that truly matter at a place that combines MIT's culture of academic excellence, exceptional resources, and strong work-life balance, apply here to learn more.
Job Description
As an Assistant Technical Staff member, you will:
- Research, design, prototype, and evaluate new approaches to securing software, embedded, and communication systems against sophisticated cyber threats.
- Develop high-quality software and experimental systems using Rust, C/C++, and other languages appropriate to the research.
- Explore problems at the intersection of AI and cybersecurity, including applications of AI to cyber analysis and defense, as well as the security and evaluation of AI-enabled systems.
- Collaborate with researchers and engineers across cybersecurity, cryptography, formal methods, AI, communications, autonomy, space, and other disciplines.
- Present results to Laboratory and government stakeholders and contribute to reports, publications, prototypes, reusable software, and the transition of successful technologies to operational use.
Required Qualifications
To thrive in this role, you should have:
- A Bachelor’s degree in Computer Science, Computer Engineering, Cybersecurity, Software Engineering, Systems Engineering, or a related technical field.
- Demonstrated proficiency in at least one of the following: + Software design, development, and testing in Rust (preferred), C/C++, or another systems programming language. + Secure software or systems security. + Systems integration, testing, and evaluation of secure systems. + Embedded systems analysis, design, and implementation.
- Substantial hands-on technical experience beyond academic coursework, typically equivalent to at least six months of full-time technical experience, gained through internships, co-ops, research appointments, employment, or comparable experience. A significant portion must be directly relevant to cybersecurity, systems security, secure software, embedded systems, or AI security.
- Strong problem-solving and technical communication skills, with the ability to work effectively in teams.
Desired Experience and Expertise
We particularly value demonstrated experience in one or more of the following:
- Systems engineering, security requirements analysis, and threat modeling.
- Security evaluation and assurance, including adversarial testing, experimental methods, security measurement, and evidence supporting security and assurance claims.
- Secure software development, operating system internals, embedded systems security, and network protocols.
- Applied cryptography and protocols, including industry and government standards.
- Static and dynamic software analysis and applied formal methods.
- AI and cybersecurity, including AI-enabled software analysis, vulnerability discovery, security testing, threat analysis, and cyber defense; and security of AI-enabled systems, including adversarial threats and evaluation of security, robustness, and resilience.
- Technical communication, including documenting designs, writing reports and research papers, and presenting technical results.
We do not expect candidates to have expertise in all of these areas and welcome candidates interested in developing deeper expertise through hands-on research.
Recent Graduate Hiring Range: $100,200 - $120,000
Experienced Hiring Range: $100,200 - $150,000
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: 43403
Salary Context
This $100K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($125K) sits 41% below the category median. Disclosed range: $100K to $150K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
MIT Lincoln Laboratory AI Hiring
MIT Lincoln Laboratory has 4 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer, Research Engineer. Positions span Lexington, MA, US, Lexington, MA. Compensation range: $150K - $220K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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