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Associate Engineering Jobs in Chestnut Hill, MA (NOW HIRING)

NorthEastern

Associate AI Engineer

Boston, MA · On-site

About the Opportunity JOB SUMMARY The Associate AI Engineer will be responsible for designing ... Software Engineering: Excellent software development skills with proficiency in Python, TensorFlow ...

Integer

Associate EHSS Engineer

Hudson, MA

Having earned a bachelor's degree in environmental, natural, or equivalent science, engineering ... Or an associate's degree in lieu of a Bachelor's degree with 1-3 years of experience or OHST ...

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All JobsAssociate Engineering JobsAssociate Engineering Jobs in Chestnut Hill, MA

Associate Engineering information

See Chestnut Hill, MA salary details

$45.3K

$90.2K

$144K

How much do associate engineering jobs pay per year?

As of Aug 23, 2026, the average yearly pay for associate engineering in Chestnut Hill, MA is $90,152.00, according to ZipRecruiter salary data. Most workers in this role earn between $71,500.00 and $103,600.00 per year, depending on experience, location, and employer.

What does an associate engineer do?

An Associate Engineer is an entry-level professional who assists in designing, developing, and maintaining engineering projects. They work under the supervision of senior engineers, helping with technical tasks such as drafting plans, conducting tests, and collecting data. Their responsibilities may also include troubleshooting issues, preparing reports, and ensuring projects comply with industry standards. Associate Engineers typically work in fields like civil, mechanical, electrical, or software engineering.

What are the key skills and qualifications needed to thrive as an associate engineer?

To thrive as an Associate Engineer, you need a solid understanding of engineering principles, problem-solving skills, and typically a bachelor's degree in engineering or a related field. Familiarity with industry-standard software such as AutoCAD, SolidWorks, or MATLAB, and sometimes certifications like EIT (Engineer-in-Training), are commonly required. Strong teamwork, adaptability, and effective communication are soft skills that help you stand out in this role. These skills and qualities are vital for successfully executing technical tasks, collaborating on projects, and supporting senior engineers in achieving organizational goals.

What are some typical challenges associate engineers face when transitioning from academic projects to real-world engineering teams?

Associate Engineers often find that moving from academic projects to professional engineering teams involves adapting to faster-paced timelines, working within established processes, and collaborating with colleagues from various disciplines. Unlike academic work, where projects might be more theoretical or self-directed, real-world engineering requires balancing multiple tasks, following industry standards, and communicating effectively to ensure project success. Over time, most associate engineers develop strong problem-solving and teamwork skills, which are essential for advancement and continued success in the field.

What is the difference between Associate Engineering vs Mechanical Engineering?

AspectAssociate EngineeringMechanical Engineering
Required CredentialsAssociate degree or diploma, some certificationsBachelor's degree in Mechanical Engineering, licensure often not required initially
Work EnvironmentEntry-level, technical support, manufacturing, constructionDesign, analysis, research, manufacturing, often more autonomous
Employer & Industry UsageConstruction firms, manufacturing companies, government agenciesEngineering firms, manufacturing, aerospace, automotive industries

Associate Engineering roles typically require an associate degree and focus on technical support and implementation tasks. Mechanical Engineering positions usually demand a bachelor's degree and involve design, analysis, and problem-solving responsibilities. Both roles are essential in engineering projects but differ in education level, scope, and responsibilities.

What can I do with an associate engineering degree?

An associate engineering degree prepares individuals for technician, technologist, or support roles in fields such as civil, mechanical, electrical, or industrial engineering. Graduates can work in design, testing, maintenance, or manufacturing environments, often using tools like CAD software and adhering to safety standards. The degree can also serve as a stepping stone to further education or certifications in specialized engineering areas.

What is the salary of an associate engineer?

The salary of an associate engineer typically ranges from $60,000 to $80,000 annually, depending on experience, location, and industry. Entry-level positions may start lower, while experienced associates or those with specialized skills can earn higher wages.

What are the most commonly searched types of Engineering jobs in Chestnut Hill, MA?

The most popular types of Engineering jobs in Chestnut Hill, MA are:

What cities near Chestnut Hill, MA are hiring for Associate Engineering jobs?

Cities near Chestnut Hill, MA with the most Associate Engineering job openings:

Associate Engineering jobs near you
Infographic showing various Associate Engineering job openings in Chestnut Hill, MA as of August 2026, with employment types broken down into 1% As Needed, 69% Full Time, 26% Part Time, 1% Temporary, and 3% Contract. Highlights an 95% Physical, 2% Hybrid, and 3% Remote job distribution, with an average salary of $90,152 per year, or $43.3 per hour.

Associate AI Engineer

Northeastern University

Boston, MA • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 11 days ago

Job description

About the Opportunity
JOB SUMMARY
The Associate AI Engineer will be responsible for designing, developing, and implementing AI systems and data pipelines that enhance and automate university operations across multiple departments. Transforms manual processes into AI-driven solutions, focusing on building robust data pipelines, creating efficient machine learning models, and integrating AI capabilities into existing systems to improve efficiency, accuracy, and service quality while reducing operational costs, utilizing expertise in machine learning, natural language processing, data engineering, and AI system integration with existing enterprise infrastructure.
MINIMUM QUALIFICATIONS
Knowledge and skills required for this position are normally obtained through a Bachelor's degree in Linguistics, Computational Linguistics, Computer Science, or related field; with four to six years of experience working with AI or machine learning , with demonstrated success in enterprise applications. Experience in higher education or similar complex organizational environments preferred.
Other necessary skills:
  • LLM Expertise: Deep understanding of large language model capabilities, limitations, and optimal interaction patterns, with demonstrated experience designing effective prompts for enterprise applications.
  • AI/ML Development Expertise: Strong proficiency in developing and deploying machine learning models and AI systems in production environments, with deep knowledge of contemporary AI frameworks, tools, and best practices.
  • Software Engineering: Excellent software development skills with proficiency in Python, TensorFlow/PyTorch, and experience with containerized deployments and MLOps practices.
  • Data Pipeline Engineering: Extensive experience with end-to-end data pipelines, data warehousing solutions , processing frameworks, and container technologies, with proficiency in Python, SQL, and version control/CI/CD practices.
  • Machine Learning Engineering: Demonstrated experience in the full ML lifecycle including data preparation, feature engineering, model training, validation, deployment, and monitoring in production.
  • Natural Language Processing: Advanced knowledge of NLP techniques and large language models (LLMs), including prompt engineering, context management, and implementation strategies for enterprise applications.
  • Cloud Computing: Experience deploying and scaling AI systems in cloud environments, with knowledge of cloud-native AI services.
  • Solution Architecture: Ability to design scalable, secure, and efficient AI system architectures that meet enterprise requirements and performance standards.
  • System Integration: Ability to integrate AI solutions with existing enterprise systems, APIs, databases, and authentication services to create cohesive user experiences.
  • Performance Optimization: Experience optimizing AI models for both accuracy and computational efficiency in resource-constrained environments.
  • Security Awareness: Knowledge of security best practices for AI systems, including data protection, model security, and prevention of adversarial attacks.
  • Data Science: Strong understanding of data structures, algorithms, statistical analysis, and data visualization techniques relevant to AI applications.
  • AI Ethics and Governance: Understanding of ethical considerations in AI development, including bias mitigation, fairness, transparency, and compliance with relevant regulations.

KEY RESPONSIBILITIES & ACCOUNTABILITIES
AI System Design and Development
Design, develop, and implement AI solutions to automate and enhance university operations, including service desk automation, administrative task processing, and QA testing systems. Create robust, scalable architectures that integrate with existing university systems and accommodate future growth.
Data Pipeline Development and Management
Design and implement end-to-end data pipelines that efficiently collect, process, and prepare data for AI systems. Build robust ETL processes using tools like Apache Airflow, cloud services, and data warehousing solutions to ensure reliable data flow between source systems and AI applications. Implement data quality checks, monitoring, and governance practices throughout the pipeline.
Machine Learning Implementation and Fine-tuning
Develop and fine-tune machine learning models for specific university use cases, including customizing large language models through prompt engineering, transfer learning, and domain adaptation. Create efficient training pipelines and establish systematic evaluation protocols.
System Integration and Deployment
Integrate AI systems with existing university infrastructure, including identity management, knowledge bases, ticketing systems, and communication platforms. Deploy models to production environments following established MLOPs practices and ensuring appropriate monitoring.
Performance Monitoring and Optimization
Monitor AI system and data pipeline performance, detect and address drift or degradation, optimize resource utilization, and continuously improve model accuracy and efficiency based on real-world usage patterns and feedback.
Position Type
Information Technology
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:
111S
Expected Hiring Range:
$87,785.00 - $123,998.75
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.

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