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About This Role
About the Opportunity
Job Summary
This is a full\-time, one\-year term appointment with the possibility of renewal. The position is in\-person at Northeastern’s Roux Institute in Portland, Maine.
The Machine Learning Engineer (MLE) at the AI Solutions Hub (AISH), the delivery arm of Northeastern University’s Experiential AI Institute, will support the development, deployment, and maintenance of machine learning systems in collaboration with other AISH employees.
This role is intended for early\-career engineers who want to build strong foundations in MLOps, cloud\-based ML systems, and production\-oriented AI development. The MLE will contribute to ML pipelines, deployment workflows, and infrastructure components while learning best practices for scalable, reliable, and responsible AI systems.
Education \& Experience
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a closely related field.
- 0\-2 years of experience in software engineering, machine learning engineering, or applied ML projects.
- Experience may include industry work, internships, co\-ops, academic research, or applied project work.
- Exposure to cloud platforms and ML deployment concepts, tools, and services is required.
- Industry experience is preferred.
Knowledge, Skills, and Abilities
ML Engineering Foundations
- Strong programming skills in Python; comfort with software engineering practices including code review, testing, version control, and documentation.
- Working knowledge of ML workflows with emphasis on the deployment side: model packaging, serving, validation, and inference rather than research or experimentation.
- Familiarity with classical ML techniques and practical exposure to modern AI including deep learning, generative AI, and large language models.
LLM \& Agentic AI Systems
- Understanding of how LLMs are deployed, served, and integrated into applications (e.g., API\-based inference, model hosting via vLLM, TGI, or similar serving frameworks).
- Familiarity with agentic AI patterns: tool use, multi\-step reasoning, orchestration frameworks (e.g., LangGraph, CrewAI, or similar), and structured output from LLMs.
- Awareness of prompt engineering for production systems: not just conversational prompting but structured prompting for reliable, parseable outputs in automated pipelines.
- Exposure to AI\-assisted development workflows and coding agents as productivity tools.
Cloud Engineering
- Experience with at least one cloud platform (AWS, Azure, or GCP), including core compute, storage, and networking services.
- Familiarity with containerization (Docker) and container orchestration (Kubernetes).
- Awareness of infrastructure\-as\-code concepts (e.g., Terraform, CloudFormation) is preferred.
MLOps and Deployment
- Build and maintain ML deployment pipelines, including model packaging, registry management, and promotion workflows.
- Support batch and real\-time inference workflows using appropriate serving frameworks (e.g., FastAPI, TorchServe, Triton, vLLM).
- Contribute to model validation, A/B testing infrastructure, and data and model versioning practices (e.g., DVC, MLflow, Weights \& Biases).
Observability \& Production Reliability
- Help implement logging, monitoring, and alerting for deployed ML services (e.g., model latency, prediction drift, error rates).
- Contribute to structured approaches for debugging production model issues, understanding the difference between infrastructure failures and model degradation.
- Awareness of cost monitoring and resource optimization for GPU and cloud\-based ML workloads.
DevOps and Automation
- Responsible for CI/CD pipelines for ML applications, including automated testing of model artifacts and data validation.
- Contribute to reproducible environment setup and configuration management.
- Learn and apply best practices for reliability, scalability, and cost\-awareness.
Security and Responsible Engineering
- Follow established security and access control practices for ML workflows.
- Assist with implementing data privacy and governance requirements.
- Responsible for secure handling of credentials, model artifacts, and sensitive data.
- Awareness of LLM\-specific security concerns: prompt injection, data leakage, and output guardrails.
Collaboration and Communication
- Ability to clearly communicate technical decisions and tradeoffs to both technical and non\-technical audiences, with guidance.
- Collaborate effectively with cross\-functional teams including data scientists, engineers, project managers, and faculty experts.
- Willingness to participate in client meetings in a supporting role.
Preferred Experience
- Exposure to Kubernetes, GPU\-based workloads, or distributed training/inference concepts.
- Familiarity with Git\-based workflows and Agile development practices.
- Coursework or projects involving NLP, computer vision, or large language models.
- Experience with API design for ML services (REST/gRPC).
- Familiarity with vector databases, retrieval\-augmented generation (RAG), or embedding\-based search systems.
Values \& Professional Attributes
Ethical and Responsible AI
- Awareness of responsible AI principles including fairness, transparency, and responsible model use.
- Willingness to follow established governance, documentation, and review practices.
Learning and Growth Mindset
- Strong interest in machine learning systems, cloud engineering, and MLOps.
- Strong curiosity and motivation to learn new tools, techniques, and AI methods.
- Openness to feedback and mentorship.
Execution and Ownership
- Ability to manage assigned tasks, meet deadlines, and maintain high\-quality work.
- Proactive attitude and willingness to take increasing responsibility over time.
Position Type
Research
Additional Information
Northeastern University considers factors such as candidate work experience, education and skills when extending an offer.
Northeastern has a comprehensive benefits package for benefit eligible employees. This includes medical, vision, dental, paid time off, tuition assistance, wellness \& life, retirement\- as well as commuting \& transportation. Visit https://hr.northeastern.edu/benefits/ for more information.
All qualified applicants are encouraged to apply and will receive consideration for employment without regard to race, religion, color, national origin, age, sex, sexual orientation, disability status, or any other characteristic protected by applicable law.
Compensation Grade/Pay Type:
110S
Expected Hiring Range:
$76,335\.00 \- $107,823\.75
*With the pay range(s) shown above, the starting salary will depend on several factors, which may include your education, experience, location, knowledge and expertise, and skills as well as a pay comparison to similarly\-situated employees already in the role. Salary ranges are reviewed regularly and are subject to change.*
Salary Context
This $76K-$107K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Northeastern University, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($92K) sits 49% below the category median. Disclosed range: $76K to $107K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Northeastern University AI Hiring
Northeastern University has 4 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist. Positions span Portland, ME, US, Burlington, MA, US. Compensation range: $107K - $165K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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