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Machine Learning Teaching Assistant Jobs in Massachusetts

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Machine Learning Teaching Assistant information

What is a machine learning teaching assistant?

Machine Learning Teaching Assistants are individuals, often graduate students or knowledgeable undergraduates, who assist professors or instructors in teaching machine learning courses. Their responsibilities typically include helping students understand course material, grading assignments, holding office hours, and sometimes leading discussion or lab sessions. They act as a bridge between students and instructors, offering support for both theoretical concepts and practical implementation. By providing guidance and feedback, they help ensure students gain a solid understanding of machine learning principles and applications.

How does a machine learning teaching assistant typically collaborate with professors and students during a course?

As a Machine Learning Teaching Assistant, you will work closely with professors to develop and grade assignments, clarify course concepts, and facilitate discussions in lectures or lab sessions. You often serve as a bridge between students and faculty, providing guidance on programming tasks, troubleshooting code, and offering feedback on projects. Regular office hours and online forums are common venues for this support, making strong communication skills and a solid grasp of machine learning fundamentals essential. This collaborative environment helps you deepen your expertise while supporting student learning.

What are the key skills and qualifications needed to thrive as a machine learning teaching assistant, and why are they important?

To thrive as a Machine Learning Teaching Assistant, you need a solid foundation in machine learning concepts, programming (often Python), and relevant coursework or a degree in computer science or a related field. Familiarity with tools like Jupyter Notebooks, TensorFlow, PyTorch, and version control systems is commonly expected. Strong communication, patience, and organizational skills help you effectively support students and collaborate with instructors. These abilities ensure you can explain complex topics clearly, assist students efficiently, and contribute to a positive learning environment.

What are the most commonly searched types of Machine Learning Teaching jobs in Massachusetts?

The most popular types of Machine Learning Teaching jobs in Massachusetts are:

What are popular job titles related to Machine Learning Teaching Assistant jobs in Massachusetts?

For Machine Learning Teaching Assistant jobs in Massachusetts, the most frequently searched job titles are:

What job categories do people searching Machine Learning Teaching Assistant jobs in Massachusetts look for?

The top searched job categories for Machine Learning Teaching Assistant jobs in Massachusetts are:

What cities in Massachusetts are hiring for Machine Learning Teaching Assistant jobs?

Cities in Massachusetts with the most Machine Learning Teaching Assistant job openings:

Machine Learning Teaching Assistant jobs near you
Infographic showing various Machine Learning Teaching Assistant job openings in Massachusetts as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 20% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Senior Machine Learning Operations Engineer

AgZen

Somerville, MA • On-site

$114K - $156K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 5 days ago

Job description

About AgZen:
AgZen is a fast-growing precision agriculture company headquartered in Somerville, MA, built on MIT research and focused on one problem: making crop spraying more efficient. Our flagship product, RealCoverage, is the world's first system that measures and controls droplet coverage at the leaf level, giving growers real-time visibility into spray performance and cutting chemical and water use by up to 50% without sacrificing yield.
We are a small, technically deep team working at the intersection of fluid mechanics, computer vision, AI, and real agricultural environments. If you want to build technology with measurable impact on how the world grows food, this is the place to do it.
About the Role
We are looking for a sharp, tenacious, and thorough Senior Machine Learning Operations (MLOps) Engineer to join our team. As part of the the perception team, you'll own the operational layer around of machine learning models. This role will be responsible for the intake and leveraging crop protection data collected from RealCoverage units installed on sprayers all around the world which is then used improve our CV pipeline and Recommendation Engine. This role will be an essential component of AgZen's measurement focus group. Strong communication, flexibility, teamwork, the desire to take on different responsibilities and own them will all be essential skills for a successful applicant.
This role is located in Somerville, MA (Boston area) with work required to be in-person.
What You'll Do
  • Own the architecture, execution, and operational excellence of large‑scale, cloud‑native pipelines for multimodal sensor data ingestion, processing, labeling, and validation.
  • Champion model traceability by building a clear lineage for every production model. Track what data trained it, what code produced it, what validation it passed, and how it's performing. Evaluate and recommend tooling for versioning, metadata, and model registry
  • Partner with data scientists to detect data quality issues, detect drift in upstream sources, and ensure features stay fresh and reliable
  • Track model drift over weeks, flag slow degradation before it crosses a threshold, surface feature freshness problems before they cascade
  • Build diagnostic tooling to root cause pipeline and recommendation issues quickly. Ensure the right context is logged at each stage, candidates, features, serving context, and building the dashboards to tie it collectively
  • Own automated gates that block bad deployments and assist in running model issue retrospectives
  • Work with ML engineers, data engineers, and stakeholders to coordinate on post-deployment metrics, defining what metrics to collect after deployment and why they matter
  • Build tooling and support non-technical domain experts in understanding perception system performance and identifying opportunities for pipeline improvement
  • Collaborate closely with cross-functional teams of software engineers, machine learning scientists, product specialists, and researchers to design, build, and maintain robust data pipelines grounded in sound data organization, domain knowledge, and careful analysis
  • Communicate technical findings, data characteristics, and limitations clearly and effectively to both internal partners and external collaborators

What We're Looking For
Required:
  • Bachelor's or graduate degree in Computer Science, Electrical Engineering, or a closely related field
  • 5+ years of experience building large-scale distributed systems, applications, or advanced ML systems‑scale distributed systems, applications, or advanced ML systems
  • Experience with MLOps, data pipelines, and cloud distributed systems
  • Proficiency in Python for system‑level and performance‑critical implementation
  • Experience operating end‑to‑end data or ML pipelines for reliability, scale, and observability
  • Communication skills that align collaborators and drive execution across functions
  • Familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow)
  • A record of ownership, accountability, and customer‑focused engineering
  • Proven track record of designing robust frameworks with high-quality, durable APIs
  • Deep understanding of machine learning algorithms with hands‑on application
  • Expertise in building reliable, high-performance, and cost-efficient systems on modern cloud infrastructure‑performance
  • Robust SQL skills and comfort digging into data distributions, feature health, and model behavior

Preferred:
  • Experience with the field of agriculture or related fields such as environmental or life sciences
  • Experience with data science based on real-world physical sensors data
  • Experience with vision-based ML
  • Experience creating intuitive data visualization tools that make complex data approachable for non-technical users
  • Prior experience in developing machine-learning models relevant to biological or crop protection outcomes
  • Advanced scientific Python (NumPy, Pandas, scikit-learn) and hands-on experience with PyTorch and/or TensorFlow, including training and deploying neural networks
  • Experience operating recommendation systems at scale

What We Offer
  • The opportunity to make an immediate and visible impact in a fast-growing company
  • Early-employee equity
  • 401(k) with employer matching at 6 months of employment
  • 6 weeks of PTO on day one
  • 12 paid holidays
  • Medical, Dental and Vision insurance

The salary range for this position is $150,000 - $200,000 depending on skills and qualifications evaluated on a per candidate basis.

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