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Machine Learning Engineer New Grad Jobs in Chesterfield, MA

Manufacturing Inc

General Laborer

Whately, MA · On-site

$20/hr

If you have cashier experience, fast food experience, or are a new grad looking to build your ... On-the-Job Learning: Gain valuable skills in logistics, retail fulfillment, and warehouse ...

Varsity Tutors

Data Science Tutor

Northampton, MA · Remote

$18 - $40/hr

... machine learning, data visualization, SQL, Python or R programming, hypothesis testing, and ... Alaska, California, Colorado, Delaware, Hawaii, Maine, New Hampshire, North Dakota, Vermont, West ...

Myrias Optics

Senior Optical Design Engineer

Amherst, MA · On-site

$150 - $200/hr

Providing customer support for new designs and products and participating in customer meetings ... Experience with integrating machine learning and inverse design methods into metasurface design ...

Varsity Tutors

Python Tutor

Northampton, MA · Remote

$18 - $40/hr

... Python to machine learning, web scraping, scientific computing, and DevOps applications ... Alaska, California, Colorado, Delaware, Hawaii, Maine, New Hampshire, North Dakota, Vermont, West ...

Lotlinx

Market Manager - New England

Springfield, MA

$85K/yr

Today, we stand as the foremost automotive AI and machine learning powered technology, excelling in ... and engineering. * Drive dealership relationships at multiple levels. Grow market share and sales.

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All JobsMachine Learning Engineer New Grad JobsMachine Learning Engineer New Grad Jobs in Chesterfield, MA

Machine Learning Engineer New Grad information

See Chesterfield, MA salary details

$31.8K

$130.1K

$195.5K

How much do machine learning engineer new grad jobs pay per year?

As of Sep 7, 2026, the average yearly pay for machine learning engineer new grad in Chesterfield, MA is $130,071.00, according to ZipRecruiter salary data. Most workers in this role earn between $102,500.00 and $156,600.00 per year, depending on experience, location, and employer.

What is a machine learning engineer new grad?

A Machine Learning Engineer New Grad job is an entry-level role for recent graduates specializing in machine learning and artificial intelligence. It typically involves developing, training, and deploying machine learning models, working with large datasets, and optimizing algorithms for performance. New grads in this role often collaborate with data scientists, software engineers, and product teams to integrate models into applications. Employers look for proficiency in programming (Python, TensorFlow, PyTorch), a strong foundation in ML concepts, and experience with data processing. This role provides an opportunity to gain hands-on industry experience and grow technical skills in real-world applications.

What are the typical day-to-day tasks of a machine learning engineer new grad?

As a Machine Learning Engineer New Grad, your daily tasks often include collecting and preprocessing data, developing and testing machine learning models, and analyzing model performance. You may work closely with data scientists and software engineers to integrate models into production systems and address real-world business problems. Participating in team meetings, code reviews, and collaborative projects is common, providing opportunities to learn best practices and receive mentorship. This hands-on, varied workload helps you quickly build technical and collaborative skills early in your career.

What are the key skills and qualifications needed to thrive in the machine learning engineer new grad position, and why are they important?

To thrive as a Machine Learning Engineer New Grad, a strong background in computer science, statistics, and mathematics, often supported by a relevant degree, is essential. Familiarity with programming languages like Python or Java, machine learning frameworks (such as TensorFlow or PyTorch), and basic knowledge of data tools and cloud platforms is typically required. Effective problem-solving, eagerness to learn, and clear communication help new grads excel when collaborating on projects and learning from senior team members. These skills and qualities are vital for adapting quickly, contributing to team goals, and building a successful foundation in this fast-evolving technical field.

What cities near Chesterfield, MA are hiring for Machine Learning Engineer New Grad jobs?

Cities near Chesterfield, MA with the most Machine Learning Engineer New Grad job openings:

Machine Learning Engineer New Grad jobs near you

Computer Vision & AI/ML Engineer Jobs

AIToolboard

Springfield, MA • On-site

$150 - $200/hr

Other

Re-posted 3 days ago

Job description

Aqua IT Springfield, US

Full-time

About the Role

Description of Services/Responsibilities:

  • Design and execute fine-tuning pipelines for Vision-Language Models (VLMs) on domain-specific imagery datasets, including data preprocessing, training orchestration, and hyperparameter optimization
  • Develop and implement evaluation frameworks for multimodal model performance, including task-specific metrics for image understanding, visual question answering, and spatial reasoning
  • Build scalable training infrastructure on AWS (SageMaker, EC2 GPU instances) for distributed fine-tuning of large multimodal models
  • Engineer data pipelines for curating, annotating, and transforming geospatial imagery datasets into model-ready formats for supervised and instruction-tuning workflows
  • Collaborate with applied scientists and solutions architects to iterate on model architectures, adapter strategies (LoRA/QLoRA), and inference optimization techniques
Basic Requirements
  • TS/SCI with CI Poly required with current NGA eligibility and SBU/SECNet/COE accounts
  • Must be willing to work in SCIF daily or as needed
  • 5+ years of professional machine learning engineering experience with a focus on deep learning
  • 1+ years of hands-on experience fine-tuning large foundation models (LLMs or VLMs)
  • Experience with parameter-efficient fine-tuning methods (LoRA, QLoRA, adapters)
  • Familiarity with supervised fine-tuning, instruction tuning, and RLHF/DPO alignment techniques
  • 4+ years of advanced Python development for ML workloads
  • Strong proficiency with PyTorch and the HuggingFace ecosystem (Transformers, PEFT, Datasets, Accelerate)
  • Experience with distributed training frameworks (DeepSpeed, FSDP, or Megatron)
  • 3+ years of experience with computer vision or multimodal models
  • Understanding of vision transformer architectures (ViT, CLIP, LLaVA-family models, or similar)
  • Experience processing and augmenting image datasets at scale
  • 3+ years of experience with AWS ML infrastructureSageMaker Training jobs, Processing jobs, and endpoint deploymentGPU instance selection, multi-node training, and cost optimization on EC2 (P4/P5/G5/G6e)S3 data management for large-scale training datasets
  • 2+ years of experience building ML evaluation pipelinesAutomated benchmarking, metric computation, and result analysisExperience with both quantitative metrics and qualitative/human evaluation approaches
  • Strong software engineering fundamentals (version control, testing, CI/CD for ML workflows)
Preferred Qualifications
  • 2+ years of experience with geospatial or remote sensing imagery
  • Familiarity with electro-optical and SAR satellite imagery formats and characteristics
  • Understanding of geospatial metadata, coordinate systems, and imagery preprocessing
  • Experience with model quantization and inference optimization (vLLM, TensorRT, ONNX)
  • Experience with MLOps and experiment tracking tools (MLflow, Weights & Biases, SageMaker Experiments)
  • Familiarity with data annotation platforms and active learning workflows for imagery
  • Experience with containerized ML workflows (Docker, ECR, ECS/EKS)
  • 2+ years of experience with Authority to Operate (ATO) processes in government environments
  • Implementation of NIST 800-53 controls and security compliance for ML systems
  • Experience deploying models in air-gapped or disconnected environments
  • Familiarity with multimodal evaluation benchmarks (MMMU, MMBench, GQA, or domain-specific equivalents)
  • Publications or demonstrated contributions in computer vision, VLMs, or multimodal AI
  • Experience with synthetic data generation for training data augmentation

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