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Evening Computer Vision Deep Learning Engineer Jobs in Leverett, MA

Varsity Tutors

Machine Learning Tutor

Northampton, MA · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection, cross-validation, regularization, ensemble methods, dimensionality reduction, clustering, and deep ...

Network Designs Inc.

AI Integrator

Chicopee, MA · On-site

$167K - $196K/yr

Bachelor 's degree in Engineering, Computer Science, or a related field.Minimum of 12+ years of ... AI/ML Engineering: Design, train, optimize, and deploy custom AI/ML models (including deep learning ...

Network Designs Inc.

AI Integrator #1744026

Chicopee, MA · On-site

$167K - $196K/yr

Bachelor's degree in Engineering, Computer Science, or a related field. * Minimum of 12+ years of ... AI/ML Engineering: Design, train, optimize, and deploy custom AI/ML models (including deep learning ...

Zulu Pods

Manufacturing Engineer

Northampton, MA · On-site

$72K - $93K/yr

... with Optimistic Vision . If these values describe you, please continue: We are seeking an ... You will be responsible for interpreting complex engineering drawings and CAD, programming ...

Zulu Pods

Manufacturing Engineer

Northampton, MA · On-site

$72K - $93K/yr

... with Optimistic Vision. If these values describe you, please continue: We are seeking an ... You will be responsible for interpreting complex engineering drawings and CAD, programming ...

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All JobsEvening Computer Vision Deep Learning Engineer JobsEvening Computer Vision Deep Learning Engineer Jobs in Leverett, MA

Evening Computer Vision Deep Learning Engineer information

See Leverett, MA salary details

$46.3K

$116.1K

$131.4K

How much do evening computer vision deep learning engineer jobs pay per year?

As of Aug 7, 2026, the average yearly pay for evening computer vision deep learning engineer in Leverett, MA is $116,111.00, according to ZipRecruiter salary data. Most workers in this role earn between $106,500.00 and $125,700.00 per year, depending on experience, location, and employer.

What is the difference between Evening Computer Vision Deep Learning Engineer vs Computer Vision Deep Learning Engineer?

AspectEvening Computer Vision Deep Learning EngineerComputer Vision Deep Learning Engineer
Required CredentialsBachelor's or Master's in CS, AI, or related fields; experience with deep learning frameworksBachelor's or Master's in CS, AI, or related fields; experience with deep learning frameworks
Work EnvironmentTypically evening or night shifts, often in research labs or tech companiesStandard daytime hours, in offices or remote settings
Industry UsageUsed in industries with 24/7 operations like surveillance, security, or manufacturingCommon across tech, automotive, healthcare, and research sectors

The main difference lies in work hours and shift timing. Evening Computer Vision Deep Learning Engineers work primarily during evening or night shifts, often in environments requiring 24/7 monitoring or operations. In contrast, Computer Vision Deep Learning Engineers usually work standard daytime hours. Both roles require similar skills and educational backgrounds, but their schedules and work environments differ significantly.

What cities near Leverett, MA are hiring for Evening Computer Vision Deep Learning Engineer jobs? Cities near Leverett, MA with the most Evening Computer Vision Deep Learning Engineer job openings:
Evening Computer Vision Deep Learning Engineer jobs near you

Computer Vision & AI/ML Engineer Jobs

AIToolboard

Springfield, MA • On-site

$150 - $230/hr

Other

Posted 2 days ago

New

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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