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Artificial Intelligence Scientist Jobs (NOW HIRING)

Artificial Intelligence Engineer

Bellevue, WA · On-site

$129K - $155K/yr

Artificial Intelligence Engineer Job Location: Bellevue - Washington Job Type: Contract * Lead the ... Partner with stakeholders to identify opportunities for data science to add value. * Design ...

Bachelor's degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, Engineering, Business Analytics, or a related field * Demonstrated knowledge or experience with ...

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Artificial Intelligence Scientist information

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$37.5K

$122.7K

$196.5K

How much do artificial intelligence scientist jobs pay per year?

As of Aug 8, 2026, the average yearly pay for artificial intelligence scientist in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What is the difference between Artificial Intelligence Scientist vs Data Scientist?

AspectArtificial Intelligence ScientistData Scientist
Required CredentialsMaster's or PhD in Computer Science, AI, or related fieldsMaster's or PhD in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, tech companies, AI-focused teamsBusiness, finance, healthcare, and various industries
Employer & Industry UsagePrimarily in AI research, development, and innovationData analysis, modeling, and decision-making support
Common Search & Comparison IntentUnderstanding AI-specific roles and skillsUnderstanding data analysis and insights roles

Artificial Intelligence Scientists focus on developing AI algorithms, models, and systems, often working in research or innovation settings. Data Scientists analyze large datasets to extract insights and support business decisions. While both roles require strong analytical skills and advanced degrees, AI Scientists specialize in creating intelligent systems, whereas Data Scientists focus on data analysis and visualization.

What are the key skills and qualifications needed to thrive as an artificial intelligence scientist?

To thrive as an Artificial Intelligence Scientist, you need a strong background in computer science, mathematics, and machine learning, usually demonstrated by an advanced degree (Master’s or PhD) in a related field. Expertise in programming languages like Python, familiarity with frameworks such as TensorFlow or PyTorch, and experience with data analysis tools are crucial. Critical thinking, creativity, and effective collaboration help distinguish top performers in developing innovative AI solutions. These skills and qualities are vital for designing, implementing, and refining advanced AI models that solve complex, real-world problems.

What are some common challenges faced by artificial intelligence scientists when deploying models into production environments?

Artificial Intelligence Scientists often encounter challenges when transitioning models from development to production, such as ensuring scalability, addressing data drift, and maintaining model performance over time. Integrating AI solutions with existing systems requires collaboration with software engineers and IT teams to handle infrastructure and security concerns. Additionally, monitoring deployed models for bias, fairness, and reliability is vital, as real-world data can differ significantly from training datasets. Overcoming these challenges involves continuous learning, cross-functional teamwork, and a proactive approach to model governance.

What is an artificial intelligence scientist?

Artificial Intelligence Scientists are experts who design, develop, and implement AI models and systems to solve complex problems. They conduct research to advance the field of AI, create algorithms, and work with large datasets to enable machines to perform tasks that typically require human intelligence. Their work often involves machine learning, deep learning, natural language processing, and computer vision. AI Scientists collaborate with other researchers, engineers, and stakeholders to deploy AI solutions in various industries such as healthcare, finance, and technology.
More about Artificial Intelligence Scientist jobs
What cities are hiring for Artificial Intelligence Scientist jobs? Cities with the most Artificial Intelligence Scientist job openings:
What states have the most Artificial Intelligence Scientist jobs? States with the most job openings for Artificial Intelligence Scientist jobs include:
Infographic showing various Artificial Intelligence Scientist job openings in the United States as of July 2026, with employment types broken down into 88% Full Time, 9% Part Time, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Research Scientist, Artificial Intelligence (PhD)

Synaptrix Labs

New York, NY

$85K - $150K/yr

Full-time

Re-posted 3 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