22 AI & Emerging Technologies jobs in the United States

The Virginia Department of Transportation's Information Technology Division is seeking a highly skilled Artificial Intelligence Engineer 4- Agentic Data Engineer to design, develop, train and deploy data models that leverage agentic AI that solve real-world problems.

Responsibilities:

Gather business needs and translate them to analytics.

Produce code that is clearly documented and maintain a version-control system.

Develop machine learning models that will address business needs.

Effectively communicate analytics results to a range of stakeholders.

Qualifications:

Demonstrated ability to perform research and communicate analyses to a wide range of audiences.

Co-author or principal investigator for 5 or more peer-reviewed publications using advanced computational or statistical analyses and/or advanced ML methods.

10+ years of leading data science projects or teams.

Gathers business requirements needed for analytics.

Experience mentoring or guiding junior team members.

Experience with supervised and unsupervised learning, model selection, feature engineering, and evaluation metrics.

Experience with Big Data frameworks like Spark/Databricks.

Experience with code version-control systems such as Git.

Degree in Computer Science, AI, Data Science, or a related field.

Skills: Advanced proficiency in Python and/or R Required 10 Years Knowledge of SQL Databases Required 10 Years Experience with Spark, GraphDB, Azure Databricks Required 5 Years Experience training LLMs with structured and unstructured data sets Required 4 Years Experience with Machine Learning Required 4 Years Experience with GIS spatial data Required 2 Years Ph.D. in a STEM field (Natural sciences, Computer Science, Statistics) Nice to have

Build AI SAAS Applications with cutting edge Agentic AI

Develop wrappers for OpenAI / Claude based resume filtering logic

Integrate interactive voice & video avatars (e.g., ElevenLabs, Heygen)

Build sleek UI and dashboards using low/no-code tools (Lovable.dev, Retool, Bubble, Firebase)

Add analytics, logging, and test automation

Ship an MVP in 2–4 weeks used by real users internally

1+ year experience in building SAAS applications using

low/nocode platforms like Lovable.

Github, Supabase, API integrations, Postman

reate/troubleshoot code using Cursor AI

Interactive Voice agent like ElevenLabs and its API integration

Interactive video agent like HeyGen and its API integration

ATS APIs (ZohoRecruit, Greenhouse)

1+ years of experience with Python, SQL , and API integration

1+ years of experience using project management tools (e.g., Trello, Notion, Jira)

LLM integration (OpenAI, Gemini, etc.)

Strong interest or basic working knowledge of LLM architectures (OpenAI, Mistral, Claude) & prompt engineering

Bonus: Experience with fine-tuning/customizing LLMs or working with vector databases

Passion for building with AI and rapid prototyping

Ability to work remotely with interns across time zones

~15–20 hours/week 9 -12  weeks

A Letter of Recommendation from a YC-networked founder

LinkedIn shoutout spotlighting your contributions

Priority consideration for future paid roles

The chance to ship a real-world MVP solving real user problems

There is no financial compensation associated with this role at this time.

Coders Connect is collaborating with a cutting-edge biotech startup redefining how foundational biology meets AI. Their mission? Train multimodal foundation models from real human biological data to revolutionize drug discovery.

We’re seeking a Senior Machine Learning Scientist with a deep interest in applying state-of-the-art ML techniques—like transformers and diffusion models—to problems at the frontier of computational biology.

Design and develop multimodal foundation models for gene regulation and drug response

Integrate chemical structure, protein sequences, and single-cell transcriptomics into unified models

Adopt and implement latest advances in deep learning (e.g., self-supervised learning, FSDP, dMoE)

Work in cross-functional teams with biologists, engineers, and data scientists

Drive hypothesis generation using ML and contribute to novel biological insights

PhD or equivalent practical experience in a technical/ML-focused field

Proven experience with deep learning (transformers, GNNs, SSMs, diffusion models, etc.)

Strong skills in PyTorch, JAX, or TensorFlow, and scientific libraries like NumPy, Pandas

Motivation to apply ML to real-world biological or chemical datasets

Bias toward rapid prototyping and practical outcomes

Prior work in computational biology or drug discovery

Experience with contrastive/multimodal/self-supervised learning

Familiarity with large-scale distributed training and GPU optimizations

Python, PyTorch/JAX/TensorFlow

Large-scale ML toolkits (e.g., flash attention, FSDP)

Transcriptomics, protein sequence data, chemical structure modeling

Coders Connect is partnering with an ambitious, AI-first healthtech startup that’s redefining the revenue cycle in healthcare through generative AI.

They build cutting-edge LLM-powered tools that supercharge the efficiency of medical billing and coding workflows—empowering healthcare providers to focus more on patient care by automating away complex administrative burdens.

We’re seeking a Senior/Staff AI Engineer to help deploy and customize intelligent automation solutions for healthcare clients. This is a hands-on role involving direct collaboration with customers, rapid prototyping, and technical leadership within a fast-moving team.

Lead deployment and customization of LLM-based platforms for healthcare clients

Translate customer pain points into AI-driven solutions

Prototype and iterate quickly, adapting to evolving client needs

Contribute feedback to product and core engineering teams

Work across a broad set of AI/ML technologies, adapting them to real-world contexts

Mentor peers and help shape the growing engineering culture

Experience in healthcare or healthtech

Proficiency in Python

Familiarity with neural networks or broader ML techniques

Python-centric

LLM tools and frameworks (e.g., LangChain, Pinecone, etc.)

Custom GenAI integrations

Equity in a fast-growing company

Full health benefits

Strong in-person team culture at their SF headquarters

Career growth in a cutting-edge AI environment

We are an IT Solutions Integrator/Consulting Firm helping our clients hire the right professional for an exciting long term project. Here are a few details.

We are seeking a Principal AI Engineer with strong technical depth and leadership capabilities to lead end-to-end AI/ML solution development across enterprise-scale applications. This individual will work across multi-disciplinary teams to design, develop, and operationalize advanced machine learning models that drive impactful business outcomes.

This is a senior, hands-on role focused on production-grade AI systems, best suited for those who thrive at the intersection of research, engineering, and applied business problem-solving.

Lead the design, development, training, testing, and deployment of advanced machine learning and AI models across diverse business use cases.

Translate business objectives into AI/ML strategies and technical implementations.

Collaborate with data scientists, software engineers, product managers, and business stakeholders to deliver scalable solutions.

Architect and optimize machine learning pipelines for model training, validation, and deployment using modern cloud-native tools and frameworks.

Implement and support MLOps workflows to ensure reproducibility, model versioning, monitoring, and compliance.

Champion model explainability, fairness, and compliance with regulatory guidelines in AI implementations.

Stay current with AI research and evaluate emerging technologies for strategic fit.

Mentor junior engineers and contribute to internal best practices and standards.

Master’s or PhD in Computer Science, Artificial Intelligence, Data Science, Mathematics, or related field.

10+ years of total experience in software engineering or data science, with at least 5 years focused on AI/ML.

Demonstrated experience deploying ML models in production environments using frameworks such as TensorFlow , PyTorch , or Scikit-learn .

Proficiency in Python , with strong command over libraries like Pandas, NumPy, and ML-focused packages.

Solid understanding of SQL and experience working with relational and/or NoSQL databases.

Hands-on experience with cloud platforms such as AWS , Azure , or GCP and cloud-based ML services.

Expertise in supervised and unsupervised learning , NLP , feature engineering , and model interpretability .

Strong understanding of the full ML lifecycle, including model training, hyperparameter tuning, validation, A/B testing, and deployment.

Excellent communication and cross-functional collaboration skills.

Background in financial services , including exposure to banking, lending, investments, or regulatory compliance.

Experience with MLOps tools and frameworks such as MLflow , Kubeflow , SageMaker , or TensorFlow Extended (TFX) .

Familiarity with regulatory and ethical AI concerns, including GDPR , OCC , or Fair Lending regulations.

Prior experience integrating AI solutions into enterprise-grade platforms or digital banking products.

Working knowledge of containerization (Docker), orchestration (Kubernetes), and microservices architectures.

Visionary problem-solver with a strong analytical mindset.

Collaborative team player who thrives in a dynamic, matrixed global environment.

Capable of influencing and guiding AI strategy and implementation at both technical and executive levels.

We are an IT Solutions Integrator/Consulting Firm helping our clients hire the right professional for an exciting long term project. Here are a few details.

We are looking for a Machine Learning Engineer / Data Scientist with 3–6 years of hands-on experience to join our growing data science team. You will be working on the design, development, and deployment of scalable machine learning models that drive intelligent decision-making across business units. This is an excellent opportunity to contribute to real-world ML products and shape innovative solutions in a data-driven environment.

Design and implement machine learning models to address business problems including classification, regression, and clustering.

Collaborate with cross-functional teams including data engineers, software developers, and domain experts to build and integrate ML solutions.

Perform data preprocessing, exploration, and feature engineering using tools like Pandas and NumPy .

Develop and evaluate models using libraries such as Scikit-learn , XGBoost , TensorFlow , or PyTorch .

Work with large-scale datasets to build models that are robust, interpretable, and production-ready.

Monitor model performance and assist in retraining and tuning when necessary.

Document model assumptions, development steps, and performance metrics for internal governance and review.

Bachelor’s or Master’s degree in Computer Science , Data Science , AI , Engineering , or related fields.

3–6 years of experience in machine learning or data science roles.

Proficient in Python , with strong knowledge of ML libraries like Scikit-learn, XGBoost, TensorFlow, or PyTorch .

Comfortable writing and optimizing SQL queries.

Hands-on experience with data wrangling and manipulation using Pandas or similar tools.

Solid understanding of key machine learning concepts: classification, regression, clustering, cross-validation, evaluation metrics, feature selection , and hyperparameter tuning .

Exposure to cloud platforms such as AWS , Azure , or Google Cloud Platform (GCP) .

Experience using ML tools like SageMaker , Databricks , or Vertex AI .

Familiarity with MLOps concepts and tools such as MLflow , Airflow , or Kubeflow .

Domain knowledge or prior experience in financial services , especially building models for credit scoring , risk assessment , or fraud detection .

Understanding of regulatory requirements , model documentation standards , and governance protocols in the financial sector.

Exposure to model monitoring frameworks and alerting pipelines.

Experience with notebooks (Jupyter, Colab) and version control (Git).

Background in statistics or a quantitative field is a strong plus.

We are an IT Solutions Integrator/Consulting Firm helping our clients hire the right professional for an exciting long term project. Here are a few details.

We are seeking a Senior AI Engineer to join our advanced analytics and data science team, supporting high-impact machine learning initiatives across the organization. This role is ideal for someone passionate about designing, developing, and deploying scalable AI/ML solutions in real-world, production-grade environments.

Design, build, and deploy machine learning models to solve business-critical problems in areas such as risk modeling, financial forecasting, and customer analytics.

Collaborate with data engineers, product managers, and business stakeholders to understand requirements and deliver scalable solutions.

Use MLOps practices and tools to deploy, monitor, and maintain ML pipelines in production.

Work with large-scale datasets using SQL, Pandas, or Apache Spark for feature engineering, model training, and evaluation.

Perform rigorous model validation, testing, and documentation to ensure reliability, accuracy, and compliance.

Implement interpretability techniques and explainable AI approaches to drive stakeholder trust and regulatory compliance.

Ensure continuous performance tracking, re-training, and improvement of deployed models.

Bachelor’s or Master’s degree in Computer Science , Machine Learning , Data Science , or a closely related field.

6 to 9 years of experience in AI/ML engineering or data science roles.

Proficiency in Python and experience with ML libraries and frameworks such as TensorFlow, PyTorch, Scikit-learn, and XGBoost .

Strong knowledge of SQL and hands-on experience with data processing tools like Pandas or Spark .

Demonstrated experience deploying ML models in cloud environments such as AWS, GCP, or Azure .

Familiarity with MLOps tools like MLflow, Kubeflow, or SageMaker for model versioning and deployment.

Solid understanding of model validation , performance tuning , and monitoring techniques.

Previous work experience in financial services or banking , particularly in:

Credit scoring or underwriting

Fraud detection

Risk modeling or regulatory reporting

Experience working with big data technologies like Hadoop, Spark .

Exposure to CI/CD pipelines , Docker , and Kubernetes for containerized model deployment.

Awareness of Responsible AI principles, including fairness , bias detection , and model explainability .

Contributions to open-source AI/ML projects or published research.

Experience with time series modeling, NLP, or deep learning techniques.