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Entry Level Artificial Intelligence Testing Jobs

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Entry Level Artificial Intelligence Testing information

What are the key skills and qualifications needed to thrive as an Entry Level Artificial Intelligence Tester, and why are they important?

To thrive as an Entry Level Artificial Intelligence Tester, you need a basic understanding of software testing principles, familiarity with AI/ML concepts, and a relevant degree in computer science or a related field. Experience with testing tools such as Selenium, Jupyter Notebooks, and version control systems like Git is often expected, along with knowledge of Python or similar languages. Attention to detail, analytical thinking, and effective communication are critical soft skills for identifying issues and collaborating with development teams. These competencies ensure the quality and reliability of AI systems, supporting robust product outcomes and continuous improvement.

What are entry level artificial intelligence testing jobs?

Entry level artificial intelligence (AI) testing jobs involve evaluating and validating AI models, algorithms, or systems to ensure they function as intended. These roles may include tasks such as creating and running test cases, reporting bugs, analyzing test results, and collaborating with development teams to improve AI performance and reliability. Entry level testers often work with machine learning models, natural language processing tools, or computer vision systems, and typically require some foundational knowledge in programming, data analysis, or software quality assurance.

What is the difference between Entry Level Artificial Intelligence Testing vs Entry Level Data Analyst?

AspectEntry Level Artificial Intelligence TestingEntry Level Data Analyst
Required CredentialsBachelor's in CS, AI, or related field; familiarity with AI toolsBachelor's in Data Science, Statistics, or related field; proficiency in Excel and SQL
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, healthcare sectors
Employer & Industry UsageAI development teams, software companiesData-driven organizations across industries
Common Search & ComparisonYesYes

Entry Level Artificial Intelligence Testing focuses on evaluating AI models and algorithms, often requiring knowledge of AI tools and programming. Entry Level Data Analysts analyze data sets to derive insights, emphasizing statistical skills and data visualization. While both roles involve data handling, AI Testing centers on AI model validation, whereas Data Analysts focus on interpreting data for business decisions.

What are some common challenges faced by entry-level professionals in Artificial Intelligence Testing, and how can they overcome them?

Entry-level AI testers often encounter challenges such as understanding complex machine learning models, interpreting unpredictable outputs, and adapting to rapidly evolving testing tools and frameworks. To overcome these hurdles, it’s essential to build a solid foundation in both software testing principles and basic AI concepts. Proactively collaborating with data scientists and developers, seeking mentorship, and engaging in continuous learning through workshops or online courses can help new testers develop confidence, stay updated, and contribute effectively to the team.
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Infographic showing various Entry Level Artificial Intelligence Testing job openings in the United States as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Research Scientist, Artificial Intelligence (PhD)

Synaptrix Labs

New York, NY

$85K - $150K/yr

Other

PTO

Re-posted 8 days ago


Job description

About Synaptrix Labs Inc.

Synaptrix is on a mission to revolutionize brain-computer interfaces through non-invasive approaches. We believe that the power to diagnose and treat neurological conditions safely, and to expand human potential, will become a reality with the right fusion of deep learning, signal processing, and computational neuroscience.

We're seeking a full time Research Scientist, Artificial Intelligence (PhD) to join our growing team of researchers & engineers. If you're passionate about shaping the future of brain-computer interfaces and excited by the potential of deep learning in neurotechnology, we want to hear from you!

Responsibilities:

  • Design, prototype, and optimize state-of-the-art AI systems for neural decoding, including diffusion models, graph neural networks, contrastive/self-supervised frameworks, and transformer-based sequence models.
  • Conduct foundational research on neural time-series representation learning: build architectures that extract latent dynamics from EEG, EMG, or related biosignals.
  • Develop high-fidelity simulation environments for testing decoding algorithms, incorporating stochastic signal noise and realistic biophysical constraints.
  • Scale model training across multi-GPU and multi-node clusters using PyTorch Distributed, DeepSpeed, or JAX/Flax; profile and tune system performance for sub-10 ms inference latency.
  • Build and maintain end-to-end research pipelines for large-scale signal datasets, including preprocessing, artifact rejection, and multimodal fusion with video, audio, and IMU data.
  • Collaborate with neuroscientists and hardware engineers to integrate learned models into real-time BCI control loops and embedded systems.
  • Contribute to core ML infrastructure: experiment tracking, model versioning, dataset lineage, and reproducibility standards.
  • Publish at top-tier ML or neurotech venues (NeurIPS, ICLR, Nature Neuro, EMBC) and present findings to the research community.

Minimum Qualifications:

  • PhD or equivalent deep technical expertise in Machine Learning, Artificial Intelligence, Computer Science, Computational Neuroscience, or related fields.
  • Strong command of PyTorch or JAX, with experience implementing custom training loops, loss functions, and model architectures.
  • Proven ability to conduct end-to-end research, from conceptual design to reproducible experiments and evaluation.
  • Strong mathematical foundations in linear algebra, probability, optimization, and information theory.
  • Experience working with high-dimensional time-series or sensory data (EEG, speech, video, motion capture, etc.).
  • Skilled in Python, NumPy, Pandas, and scientific computing workflows; experience with CUDA or low-level GPU debugging is highly valued.
  • Demonstrated ability to operate independently on open-ended problems and drive original research with limited supervision.

Preferred Qualifications:

  • Deep familiarity with neural signal modeling, neural decoding, or biosignal preprocessing (EEG/MEG/ECoG/EMG).
  • Experience designing self-supervised or generative models (diffusion, VAEs, contrastive, masked modeling) for noisy, non-stationary data.
  • Background in reinforcement learning, optimal control, or human-in-the-loop systems, especially in continuous domains.
  • Publications or preprints in top venues (NeurIPS, ICML, ICLR, CVPR, EMBC, Nature Neuro).
  • Familiarity with distributed training, mixed-precision, multi-GPU orchestration, and cloud ML infrastructure (AWS/GCP/Azure).
  • Contributions to open-source ML frameworks or custom CUDA kernels.
  • Understanding of neural signal acquisition hardware, embedded inference, or edge ML deployment.
  • Track record of curiosity-driven, independent research resulting in practical systems or open-source codebases.

About our Culture:

At Synaptrix Labs, we celebrate curiosity, open collaboration, and scientific rigor. Our interdisciplinary team spans neuroscience, AI, and clinical research, and we are united by the belief that non-invasive BCI is the key to unlocking a new era in healthcare, accessibility, and human augmentation.

Expected Compensation:

The base salary for this role is anticipated to fall within the following range. Actual compensation will depend on your experience, technical expertise, and relevant education or training. In addition to base pay, Synaptrix offers equity to all full-time employees, reflecting our commitment to shared success and long-term company growth.

Base Salary Range:

$85,000 - $150,000 USD

What We Offer:

  • An opportunity to change the world and work with some of the smartest and most talented experts from different fields
  • Growth potential; we rapidly advance team members who have an outsized impact
  • Paid holidays, unlimited PTO