We’re seeking a Data & AI Engineer to develop intelligent data pipelines and analytics solutions that power smarter decisions across silicon design, verification, and manufacturing. You’ll transform engineering data into actionable insights through automation, modeling, and visualization.

Responsibilities

• Build and maintain data pipelines to support machine learning and analytics workflows.
• Collect, clean, and transform large, complex datasets from engineering environments.
• Develop and train predictive models for yield, performance, and anomaly detection.
• Automate recurring data analysis tasks and integrate models into engineering processes.
• Collaborate with design and software teams to embed AI-driven insights into products.
• Create dashboards and visualization tools for reporting and decision-making.
• Document code, models, and processes for transparency and reproducibility.

Qualifications

• Proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Strong skills in data manipulation (Pandas, NumPy, SQL).
• Experience with workflow orchestration (Airflow, Spark, or similar).
• 3–5 years of experience in data engineering or applied AI.
• Bachelor’s degree in Electrical Engineering, Computer Science, or related field.

Preferred / Plus

• Familiarity with semiconductor design, verification, or manufacturing datasets.
• Understanding of statistical modeling and predictive maintenance.
• Experience with cloud environments (AWS, Azure, GCP) and version control (Git).
• Knowledge of MLOps principles (deployment, monitoring, CI/CD).

We are looking for a highly motivated and results-driven Head of Data to join our team full-time. In this strategic role, you will shape the vision, architecture, and delivery frameworks for Data Engineering, Data Science, Quant, and Data Analytics, unifying these teams into a single, high-impact function. This is a key leadership position, guiding both technical and managerial directions for our data organization.

We drive fintech innovation through deep analytical expertise and a data-first, engineering-driven approach.

Key Responsibilities

Data & Quant Leadership

• Define and lead the strategy across Data Engineering, Data Science, Quant, and Data Analytics, focusing on high business impact
• Build and manage cross-functional teams, including quants, data scientists, engineers, and analysts
• Drive advanced analytics, ML strategy, feature engineering, and model development with a focus on responsible AI
• Collaborate with stakeholders to turn complex business challenges into data-driven solutions

Data Engineering Leadership

• Design and evolve a scalable, reliable, and maintainable data platform architecture
• Oversee development of robust ETL/ELT pipelines and real-time data streams
• Establish engineering best practices: code reviews, CI/CD, data contracts, observability
• Drive technology selection and resource planning for ClickHouse, Spark, and supporting infrastructure
• Ensure data quality through monitoring, alerting, SLA ownership, and remediation processes

People & Cross-Functional Leadership

• Communicate effectively across technical and non-technical teams to influence decisions
• Lead cross-team processes (e.g., grooming, design reviews, retrospectives)
• Mentor team members, create growth plans, and foster a culture of psychological safety, accountability, and innovation

AI & LLM Strategy

• Define and drive GenAI and LLM strategy: identify high-value use cases, evaluate model providers (OpenAI, Anthropic, open-source), and lead end-to-end implementation
• Architect and oversee LLM-powered products: RAG pipelines, AI agents, and intelligent automation workflows integrated into core business processes
• Lead adoption of agentic AI frameworks (LangChain, LlamaIndex, AutoGen) to build autonomous multi-step reasoning systems
• Establish MLOps and LLMOps best practices: model versioning, evaluation frameworks, prompt management, and monitoring for drift and hallucinations
• Drive responsible AI governance: bias detection, explainability (SHAP, LIME), fairness auditing, and compliance with emerging AI regulations
• Champion AI-assisted development workflows (GitHub Copilot, Cursor) and foster an AI-augmented engineering culture across data teams
• Evaluate and integrate vector databases (Pinecone, Weaviate, pgvector) and embedding strategies to power semantic search and knowledge retrieval
• Collaborate with product and engineering to productionize AI features with robust feedback loops, A/B testing, and continuous model improvement

Requirements

• 7+ years of hands-on experience across Data Science, Quant, Data Engineering, delivering end-to-end solutions
• 3+ years of managerial experience, leading data teams including quants and data scientists
• Bachelor’s or Master’s degree in Computer Science, Mathematics, Physics, Engineering, or a related field
• Strong programming skills in Python, with experience writing clean, production-grade code
• Solid understanding of software engineering best practices (CI/CD, testing, code reviews, clean architecture)
• Practical experience with ML libraries and platforms (e.g., scikit-learn, XGBoost, PySpark)
• Deep understanding of core ML algorithms: regression, gradient boosting, time series, etc.
• Strong foundation in mathematical statistics, probability theory, and quantitative modeling
• Hands-on experience with LLMs, prompt engineering, and building production-grade AI systems (RAG, agents, fine-tuning)
• Familiarity with LLMOps tooling: experiment tracking (MLflow, W&B), vector stores, evaluation frameworks (RAGAS, DeepEval)
• Proficient in SQL and experience with analytical and OLAP databases
• English Upper-Intermediate

Nice to Have

• Experience with ClickHouse or other OLAP databases
• Background in trading or fintech
• Experience analyzing and modeling time series or high-frequency data
• Familiarity with anti-fraud systems, risk modeling, or portfolio analytics

We offer

• 20 paid vacation days per year
• 10 paid sick leave days per year
• Public holidays according to current legislation
• Medical insurance
• Opportunity to work remotely
• Professional education budget
• Language learning budget
• Wellness budget (gym membership, sports gear and related expenses)

AI Course Builder – Business & Finance Applications

About Wall Street Prep

Founded in 2004 by investment bankers, Wall Street Prep (WSP) is a leading provider of professional skills training in financial and valuation modeling. Our mission is to bridge the gap between academia and the real world by equipping finance professionals and students with practical, hands-on skills.

We partner with top-tier investment banks, private equity and investment management firms, Fortune 500 companies, and elite undergraduate and MBA programs. Our training is delivered through engaging in-person classroom sessions and via our industry-leading online learning platform, all designed by experienced finance professionals.

As artificial intelligence rapidly transforms financial services, WSP is expanding its AI curriculum to help finance professionals integrate modern AI tools directly into their workflows.

Position Overview

Wall Street Prep is seeking AI Course Builders with strong practical experience using modern AI tools and automation workflows to help develop short, practical AI courses for finance professionals.

In this role, you will focus on designing hands-on AI learning content rather than delivering live instruction.

You will work with WSP’s learning team to develop high-impact modules that teach finance professionals how to apply AI tools to real business tasks, such as research, financial analysis, workflow automation, and productivity.

This includes building course content centered around leading AI assistants and enterprise AI platforms such as ChatGPT, Claude, Gemini, Microsoft Copilot, Perplexity, Rogo, and Shortcut, along with Python-based workflows and API integrations.

Your work will directly shape how professionals in investment banking, private equity, asset management, consulting, and corporate finance adopt AI in their day-to-day work.

Key Responsibilities

AI Course Development

Design and build practical learning modules covering topics such as:

• Foundations of generative AI and large language models
  
  
• Prompt engineering for business workflows
  
  
• AI-assisted research and knowledge synthesis
  
  
• AI-assisted financial analysis and modeling
  
  
• Workflow automation using AI tools
  
  
• AI productivity tools for analysts and professionals

Hands-On Exercise Design

Develop real-world exercises and labs that teach professionals how to use AI tools in practice, including:

• Using ChatGPT, Claude, Gemini, and Perplexity for research, analysis, and writing
  
  
• Leveraging Microsoft Copilot, Rogo, and Shortcut to automate workflows and productivity tasks
  
  
• Building prompt workflows for financial analysis and research
  
  
• Creating simple Python-based automation scripts
  
  
• Demonstrating AI-assisted coding and data analysis

Practical Use Case Development

Create applied case studies that demonstrate AI in real-world business and finance contexts, such as:

• Investment research and summarization
  
  
• Financial data analysis and visualization
  
  
• Competitive analysis
  
  
• Document review and synthesis
  
  
• Workflow automation and productivity tools

Collaboration with the Learning Team

Work with WSP’s instructors and learning designers to:

• Translate technical AI concepts into practical business workflows
  
  
• Develop structured course modules and exercises
  
  
• Update content as AI tools rapidly evolve
  
  
• Ensure courses are practical, relevant, and accessible to finance professionals

What We Are Looking For

Experience

3+ years of experience working with AI tools, machine learning, data science, or AI-enabled workflows.

Strong practical experience using modern AI assistants and enterprise AI tools such as:

• ChatGPT
  
  
• Claude
  
  
• Gemini
  
  
• Microsoft Copilot
  
  
• Perplexity
  
  
• Rogo
  
  
• Shortcut
  

Experience applying AI tools to real-world workflows such as research, analysis, automation, coding, or productivity.

Experience building technical content such as:

• technical tutorials
  
  
• course materials
  
  
• developer documentation
  
  
• technical blog posts
  
  
• learning modules or workshops

Experience in finance, consulting, or corporate environments is a plus but not required.

Technical Skills

Practical expertise using generative AI tools including:

• ChatGPT
  
  
• Claude
  
  
• Gemini
  
  
• Perplexity

Experience with AI productivity tools and workflow assistants such as:

• Microsoft Copilot
  
  
• Rogo
  
  
• Shortcut

Python programming experience strongly preferred, including libraries such as:

• Pandas
  
  
• NumPy
  
  
• Matplotlib
  
  
• scikit-learn

Experience working with AI APIs and frameworks such as:

• OpenAI API
  
  
• Hugging Face
  
  
• LangChain
  
  
• similar AI tooling

Working Arrangement

This role is project-based or part-time, focused on building course materials for upcoming AI training programs.

The position is remote-friendly.

Content developers will collaborate closely with WSP’s learning team and instructors.

Compensation

Highly competitive project-based compensation depending on experience and scope of work.

Why Join Wall Street Prep

Help shape how AI is taught to professionals across global finance.

Build practical AI learning content used by professionals at leading financial institutions.

Collaborate with a highly experienced team of finance and training experts.

Contribute to one of the fastest-growing areas of professional education.

Bioinformatics Machine Learning Intern

RefinedScience | United States (hybrid or remote)

At RefinedScience, our mission is to advance care by bringing together the best science, data and minds – disease by disease, patient by patient, cell by cell to discover pathways to life beyond disease.

What We Are Looking For

We are seeking a highly motivated Bioinformatics Machine Learning Intern to join our team. This internship is designed for Ph.D. candidates with experience applying machine learning, deep learning, or generative AI methods to single-cell omics data. You will contribute to active projects spanning single-cell biology, multiomics integration, and computational approaches to precision medicine and drug development.

Our Bioinformatics team plays a crucial role in integrating computational biology, large-scale data analysis, and machine learning to drive discoveries in precision medicine and drug development.

Key Activities

• Analyze single-cell and multiomics datasets to extract biological insights supporting precision medicine and drug development programs
• Apply and evaluate machine learning and deep learning approaches to single-cell data for tasks such as cell type classification, biomarker discovery, and patient stratification
• Explore and prototype generative AI and LLM-based approaches to accelerate biological data interpretation and scientific workflows
• Collaborate with scientists, clinicians, and data scientists to design and execute data-driven research projects
• Document and optimize computational workflows following reproducible research best practices
• Present findings through technical reports, visualizations, and presentations to cross-functional teams

Must Haves

• Current Ph.D. candidate in Bioinformatics, Computational Biology, Computer Science, Biostatistics, or a related quantitative field
• Single-cell omics experience: Demonstrated ability to process, analyze, and interpret single-cell data (scRNA-seq, scATAC-seq, CITE-seq, or spatial transcriptomics) using frameworks such as Scanpy/scverse, Seurat, or Bioconductor
• Machine learning expertise: Applied experience developing and evaluating ML/deep learning models on biological data, including neural network architectures (GNNs, transformers, autoencoders), model selection and benchmarking, and integration of ML approaches into analytical workflows
• Programming proficiency: Python and/or R for data analysis, statistical modeling, and visualization
• Statistical foundation: Understanding of statistical methods for biological data (hypothesis testing, differential expression, multiple testing correction, clustering)
• Strong problem-solving skills and ability to communicate complex insights effectively

Desired Qualifications

Machine Learning & AI

• Experience with deep learning frameworks (PyTorch, TensorFlow, JAX)
• Familiarity with graph neural networks, attention mechanisms, or transformer architectures applied to biological data
• Experience with ML experiment tracking and reproducibility (MLflow, Weights & Biases)
• Exposure to representation learning, variational autoencoders, or contrastive learning methods
• Familiarity with scikit-learn, XGBoost, or similar ML libraries
• Interest in or experience with LLMs, RAG systems, or agentic AI tooling

Bioinformatics

• Experience with multimodal single-cell integration (Seurat WNN, scvi-tools/MultiVI/totalVI, Muon)
• Familiarity with spatial transcriptomics analysis (Squidpy, cell2location, nf-core/spatialvi)
• Experience with cell-cell communication inference (CellChat, NicheNet, LIANA)

• Knowledge of drug-gene interaction resources (CMap/LINCS, OpenTargets, ChEMBL)

Engineering & Infrastructure

• Familiarity with Linux/Unix CLI and version control (Git/GitHub)
• Experience with containerization (Docker, Singularity) and environment management (conda, venv)
• Exposure to cloud computing platforms (GCP preferred)
• Familiarity with workflow managers (Nextflow, Snakemake)
• Adherence to best-practices for conduct reproducible computational research

Duration

8–10 weeks

Why You'll Love RefinedScience

Team + Values

At RefinedScience, we seamlessly integrate top-tier clinical and biological data with expert knowledge to provide unparalleled insights. We maximize patient impact with these unique insights by optimizing clinical trial probability of success and time to actionable results. We work across biopharma and we are a trusted partner in achieving better results, faster – working together to unlock strategic advantage.

Our Values

• Act with Purpose – We believe in rigor through deliberate and thoughtful actions
• Be Curious – Curiosity is the spark that ignites innovation and growth
• Take Ownership – True ownership leads to pride and commitment in the work we do
• Invest in Relationships – Building strong connections is the foundation for effective collaboration and trust for long term success
• Embrace Agility – We celebrate agile thinking, resilience, and adaptability

Compensation

• $34-$38 per hour

Job Description 

Buzz is revolutionizing the analytics and maintenance of power grid infrastructure through our advanced AI solutions. Our computer vision systems analyze critical infrastructure to enhance safety, reliability, and operational efficiency across the power grid network. We're seeking an experienced Machine Learning Engineer to help lead our computer vision initiatives. You'll drive the development of cutting-edge models for power grid analysis and provide a leadership anchor on a team of talented ML engineers.

Responsibilities

• Architect and lead end-to-end computer vision projects focused on: Equipment defect detection, Thermal anomaly identification, Vegetation encroachment monitoring, Surveillance of closed areas for human and animal intrusions
• Drive innovation by incorporating the latest advances in deep learning and generative AI to enhance model training, accuracy and reliability
• Develop production-grade Python libraries for the complete ML lifecycle
• Mentor team members and establish best practices for model development, evaluation, deployment, and monitoring
• Advocate for and uphold software quality standards within the ML team

Qualifications & Experience

• 7-10 years of industry experience in computer vision and machine learning
• Deep expertise in modern computer vision and deep neural networks including: Object detection, Semantic segmentation, Image classification, Similarity search, Vision language models
• Proven track record of deploying and maintaining ML models in production
• Expert proficiency in PyTorch, Lightning, OpenCV, and Scikit-Learn
• Proficiency in FastAPI and Pydantic
• Strong software engineering foundation including: Git version control, Test driven development (Pytest), CI/CD, ML devops, Python type hinting
• 2-3 years of proven leadership experience of technical teams

Desired Additional Experience

• Multi-modal computer vision 
• Custom object detection model development
• Generative models for data augmentation
• ML deployment on edge devices
• Extracting measurements from GIS and/or drone metadata enriched imagery
• Model quantization
• Systematic hyperparameter tuning

Additional information:

• This position does not include sponsorship for United States work authorization.

Join the core team at Eclipse, where we’re building an AI agent-first marketplace that connects intelligence with real-world tasks, starting with data collection and labeling. We are seeking a Data Scientist to establish the foundation for how our data is labeled, processed, and prepared for consumption by next-generation Large Language Models (LLMs). Your work will be critical in transforming our raw data collections into valuable, AI-ready datasets.

Qualifications 

• Proven experience as a Data Scientist or Machine Learning Engineer with a focus on data quality and preparation.
• Strong understanding of data labeling methodologies and hands-on experience with data annotation platforms and workflows.
• Demonstrated experience preparing datasets for training and fine-tuning Large Language Models (LLMs), including knowledge of techniques like tokenization, embeddings, and NER.
• Proficiency in Python and common data science libraries (e.g., Pandas, NumPy, Scikit-learn, spaCy, Hugging Face).
• Experience using APIs/SDKs to automate data annotation and active learning loops.
• Excellent communication skills, with an ability to create clear documentation for technical and non-technical audiences.

Responsibilities 

• Develop Data Labeling Strategies: Design and document a formal data annotation strategy, including clear, scalable, and efficient guidelines for labeling our data. Define and enforce quality metrics, including inter-annotator agreement.
• Optimize for LLM Consumption: Research, define, and prototype the optimal data formats, structures, and pre-processing steps required for fine-tuning and training LLMs on our datasets.
• Data Quality Analysis: Establish automated processes and metrics to analyze the quality of both raw and labeled data, providing feedback to improve our data collection and labeling workflows.
• Collaborate with Engineering: Work closely with the engineering team to guide the implementation of data processing pipelines and ensure the data infrastructure meets the needs of ML applications.

Nice-to-Haves

• Experience with audio data processing and relevant libraries.
• Familiarity with data annotation platforms and tools.
• Knowledge of modern MLOps principles and practices.
• Experience with large language model data curation and Reinforcement Learning from Human Feedback (RLHF) pipelines.

Join the Eclipse team!

Eclipse is building the fastest Ethereum Layer 2, powered by the Solana VM. Our general-purpose L2 combines the best of the modular stack without sacrificing UX or fragmenting liquidity. On top of this foundation, we’re building apps in-house and iterating quickly to find breakout consumer and AI experiences. We’re backed by top investors including Polychain, Tribe Capital, Placeholder, and DBA.

• Opportunity. We believe blockchains should be fast AND highly usable. You’ll do high-impact work to enhance Ethereum’s scalability, shaping the future of crypto
• Flexibility. We collaborate synchronously and asynchronously, across weekly all-hands meetings, Slack messaging, and quarterly in-person meetups
• Team. Our founding team has experience launching and scaling blue-chip projects such as dYdX, Uniswap, and zkSync. We’re backed by leading funds and leaders including Polychain, Tribe, Placeholder, DBA, Mustafa Al-Bassam, Tarun Chitra, Meltem Demirors, and others
• Culture. As an early member of our team, you’ll have a unique opportunity to help shape our culture. We value intellectual honesty, bias towards action, and believe every member plays a key role in achieving our ambitious goals
• Compensation. You’ll receive a competitive salary + equity + benefits package.

Eclipse Laboratories is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), national origin, age, disability, genetic information, veteran status, or any other status protected by applicable laws or regulations. All employment decisions are based on qualifications, merit, and business need.