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Machine Learning Developer Intern Jobs in Williamstown, MA

SymphonyAI

AI Platform Architect

Albany, NY · On-site

This role sits at the intersection of data, machine learning, engineering, and product, translating business needs into robust, secure, and scalable AI architectures. The AI Architect will define ...

DataAnnotation

SDLC Engineer - AI Trainer

Albany, NY · Remote

$50 - $100/hr

As a DataAnnotation's coder, you'll be part of a growing community of over 100,000 professionals -- including front-end, back-end, full-stack, machine learning, and other engineers -- who are driving ...

GCOM

HBITS-02-11172

Troy, NY

Perform machine and deep learning models: neural network model, text classification using LSTM and ... Applies engineering disciplines to cloud computing and brings a systematic approach to concerns of ...

DataAnnotation

QA Engineer - AI Trainer

Albany, NY · Remote

$50 - $100/hr

As a DataAnnotation's coder, you'll be part of a growing community of over 100,000 professionals -- including front-end, back-end, full-stack, machine learning, and other engineers -- who are driving ...

Stantec

Fire Protection EIT

Albany, NY · On-site

$80K - $108K/yr

The role applies professional concepts and expands upon learning while performing various phases of ... Engineer in Training (EIT) / Engineering Intern (EI) preferred, working towards obtaining Fire ...

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All JobsMachine Learning Developer Intern JobsMachine Learning Developer Intern Jobs in Williamstown, MA

Machine Learning Developer Intern information

See Williamstown, MA salary details

$25.3K

$42.3K

$87.3K

How much do machine learning developer intern jobs pay per year?

As of Jul 9, 2026, the average yearly pay for machine learning developer intern in Williamstown, MA is $42,267.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,300.00 and $45,700.00 per year, depending on experience, location, and employer.

How do Machine Learning Developer Interns typically collaborate with data scientists and engineers during their internship?

Machine Learning Developer Interns often work closely with data scientists to understand the problem domain, gather relevant datasets, and select appropriate models. They also collaborate with software engineers to integrate machine learning solutions into existing systems, ensuring scalability and performance. Regular communication through stand-up meetings, code reviews, and collaborative platforms is common, allowing interns to learn best practices and receive feedback on their work. This teamwork not only enhances technical skills but also provides valuable exposure to real-world deployment and project lifecycle management.

What does a Machine Learning Developer Intern do?

A Machine Learning Developer Intern assists with developing, testing, and implementing machine learning models and algorithms under the guidance of experienced engineers or data scientists. Their tasks may include data preprocessing, model training, evaluating model performance, and helping deploy models into production environments. Interns often collaborate with team members to solve real-world problems using machine learning techniques and may also assist in researching new methodologies or optimizing existing solutions. This role provides hands-on experience in coding, data analysis, and applying theoretical concepts to practical scenarios.

What are the key skills and qualifications needed to thrive as a Machine Learning Developer Intern, and why are they important?

To thrive as a Machine Learning Developer Intern, you need a solid understanding of programming (especially Python), statistics, and machine learning concepts, often supported by coursework or relevant project experience. Familiarity with ML frameworks like TensorFlow or PyTorch, and tools such as Jupyter Notebooks and version control systems like Git, is typically expected. Strong analytical thinking, eagerness to learn, and effective communication help interns contribute to team projects and adapt quickly. These skills are essential for solving real-world problems, collaborating with teams, and building a foundation for a successful career in machine learning.

What is the difference between Machine Learning Developer Intern vs Data Scientist Intern?

AspectMachine Learning Developer InternData Scientist Intern
Required CredentialsTypically pursuing or recently completed a degree in Computer Science, Data Science, or related fields; knowledge of programming languages like Python or JavaSimilar educational background; strong skills in statistics, programming, and data analysis
Work EnvironmentHands-on experience with ML models, algorithms, and software development in tech or research settingsData analysis, visualization, and interpretation in business or research contexts
Employer & Industry UsageTech companies, startups, research labs focusing on AI/ML projectsBusiness, finance, healthcare, and research organizations analyzing large datasets

Both roles involve working with data and programming, but Machine Learning Developer Interns focus more on building and deploying ML models, while Data Scientist Interns emphasize data analysis and insights. The roles often overlap, especially in tech environments, but their core tasks differ slightly.

Machine Learning Developer Intern jobs near you
AI Platform Architect

AI Platform Architect

SymphonyAI

Albany, NY • On-site

Full-time

Re-posted 25 days ago

Job description

Job Description
We are seeking an experienced AI Architect to design, govern, and scale end-to-end AI solutions that deliver measurable business outcomes. This role sits at the intersection of data, machine learning, engineering, and product, translating business needs into robust, secure, and scalable AI architectures.
The AI Architect will define reference architectures, select platforms and tools, and guide teams in building production-grade AI systems across the enterprise.
Key Responsibilities
Platform Architecture & Vision
  • Own the end-to-end architecture for the AI platform, spanning:
    • Agent frameworks and orchestration layers
    • Semantic and knowledge graph foundations
    • Data and signal ingestion fabric
    • Model, reasoning, and tool-execution services
    • Product and solution enablement layers
  • Establish modular, extensible reference architectures enabling rapid product and solution development.
  • Drive architectural consistency across teams building on AI Platform.

2.Agentic & Knowledge-Driven AI Systems
  • Architect agent-based systems capable of reasoning, planning, retrieval, and execution across enterprise workflows.
  • Design hybrid AI architectures combining:
    • LLMs and multi-model stacks
    • Knowledge graphs and ontologies
    • Vector retrieval and semantic search
    • Deterministic services and enterprise APIs
  • Lead the evolution of CINDE's semantic layer and retail knowledge foundation.

3. Solution Architecture & Business Enablement
  • Partner with Product, Engineering, and Business leaders to translate strategy into scalable technical systems.
  • Architect AI solutions across retail and CPG domains, including:
    • Forecasting, demand intelligence, and optimization
    • Price, promotion, and assortment intelligence
    • Shopper personalization and retail media
    • Store, shelf, and inventory intelligence
    • Enterprise revenue and decision automation
  • Ensure architectures directly support revenue growth, product velocity, operational efficiency, and customer impact.

4. AI Platform Engineering, MLOps & LLMOps
  • Define CINDE standards for:
    • Model lifecycle management
    • Agent deployment and orchestration
    • Prompt, workflow, and tool governance
    • Experimentation and evaluation pipelines
  • Design scalable MLOps / LLMOps / AgentOps foundations:
    • CI/CD for AI and agent workflows
    • Observability, telemetry, and quality measurement
    • Versioning, monitoring, drift detection, and retraining

5. Governance, Security & Responsible AI
  • Embed enterprise-grade security, privacy, and compliance into CINDE architecture.
  • Define and enforce Responsible AI frameworks across the platform:
    • Explainability, traceability, and auditability
    • Bias, safety, and risk controls
    • Regulatory and customer-facing compliance readiness
  • Partner closely with Security, Legal, and Compliance leaders.

6. Technical Leadership & Influence
  • Serve as a technical north star across product and engineering organizations.
  • Mentor senior engineers, architects, and data scientists.
  • Influence platform decisions across multiple business units without direct authority.
  • Continuously assess emerging technologies and translate them into advantage.

Required Technical Skills
Cloud & Platform Engineering
  • Deep experience with AWS, Azure, or GCP AI platforms
  • Kubernetes, containerized AI workloads, and distributed systems
  • Infrastructure as Code and environment automation

Data, Knowledge & Signal Fabric
  • Enterprise data lakes and lakehouse platforms
  • Streaming and real-time signal architectures
  • Strong distributed data processing background
  • Knowledge graph platforms, semantic modeling, and ontologies

AI, ML & Agentic Systems
  • Expert-level Python
  • Production ML frameworks (PyTorch, TensorFlow, scikit-learn)
  • Agent frameworks and orchestration platforms
  • Multi-model system design

GenAI & Knowledge-Grounded AI
  • Commercial and open-source LLM ecosystems
  • RAG and hybrid retrieval architectures
  • Vector databases and embedding systems
  • Fine-tuning, evaluation, and prompt lifecycle managemen

MLOps / LLMOps / AgentOps
  • MLflow, Kubeflow, or equivalent platforms
  • CI/CD for AI workloads
  • Model and agent observability, testing, and governance

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