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).

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

Location: Remote

Duration: 2–4 months (project-based)

Type: Contract / Research Collaboration (Paid)

About the Project

We are looking for a Master’s or PhD student to work on fine-tuning large language models (LLMs) for domain-specific tasks. The goal is to take an existing pretrained model (e.g., Meta AI’s LLaMA-class models or similar) and specialize it for a narrow, high-value use case using efficient fine-tuning techniques.

This is a hands-on applied project designed for someone who wants real-world experience deploying and optimising LLM systems.

Help drive the next wave of applied AI by demonstrating how fine-tuned LLMs can unlock advanced, real-world use cases beyond general-purpose foundation models. Organizations that require domain-specific accuracy, self-hosted deployments, customisable workflows, or performance beyond out-of-the-box capabilities increasingly rely on fine-tuned models to meet those needs.

Through this project, you will contribute to building specialised AI systems that deliver improved accuracy, efficiency, and control compared to out-of-the-box models. You will also help bridge the gap between academic knowledge and real-world application by applying fine-tuning techniques to solve concrete business problems.

What You’ll Work On

• Fine-tuning pre-trained LLMs on small to medium datasets (500–20k examples)
• Implementing parameter-efficient fine-tuning (e.g., LoRA-style methods)
• Optimising training for cost and performance
• Running experiments on GPU cloud infrastructure
• Evaluating model performance and tradeoffs (specialisation vs generalisation)
• Deploying fine-tuned models for inference

Experience

• Strong Python skills
• Experience with deep learning frameworks: PyTorch (preferred) or TensorFlow
• Experience with Hugging Face Transformers or similar ecosystems
• Hands-on experience training or fine-tuning transformer models on GPUs (local or cloud-based)
• Previous experience using cloud platforms for model training or deployment (e.g., AWS, GCP, Azure, RunPod or similar GPU providers)
• Experience working with or fine-tuning open-weight LLM families (Gemma-3, Qwen-3.5, Llama 4, GPT-OSS, Mistral...)
• Hands-on experience with LoRA

Understanding of:

• Fine-tuning vs pretraining
• Overfitting and generalization
• Model evaluation
• Strong business awareness: ability to understand the context of the fine-tuning task and translate domain requirements into clear modeling objectives

What you bring

• MSc or PhD student in Computer Science, Machine Learning, AI, or related field
• Alternatively, 6 months of hands-on experience training and fine-tuning deep learning models
• Has worked on LLMs in research or industry
• Has fine-tuned at least one transformer model
• Comfortable working independently
• Interested in applied AI and real-world constraints (cost, latency, memory)

What You’ll Gain

• Real-world experience fine-tuning large models (30B–100B parameter class)
• Exposure to production constraints and deployment
• Opportunity to co-author technical writeups if applicable
• Strong applied portfolio project

What We Offer

• 100% Remote Work: Work from anywhere with flexibility and autonomy 
• Dynamic, High-Impact Projects: Work on cutting-edge ML and GenAI solutions across diverse industries
• International Clients: Collaborate with global organizations and solve real-world challenges at scale
• Urban Sports Club Membership: Supporting your physical and mental wellbeing
• Monthly Bolt Credits: For rides
• Company Events & Offsites: Regular team gatherings to connect, collaborate, and celebrate

The Role

• Contribute to cutting-edge research in scalable, distributed machine learning systems alongside experienced researchers and engineers. Explore new ways of building and verifying neural networks that operate across huge, decentralised, topologies of heterogenous devices.

Responsibilities

• Contribute to original research in deep learning with a focus on modular architectures, verifiability, continual learning, and scale
• Design and prototype novel neural network architectures for decentralized compute environments
• Contribute to joint publications and projects in collaboration with academic and industry researchers targeting top-tier AI venues such as NeurIPS, ICML, and ICLR

Competencies

Must Have 

• Currently enrolled in a PhD program (or, in exceptional cases, in a Master’s program) in Computer Science, Machine Learning, or a related field 
• Prior experience conducting original research, ideally with authorship or co-authorship on ML papers
• Strong understanding of deep learning fundamentals and experience working with in at least one major framework, e.g. PyTorch, JAX, or TensorFlow
• Self-directed, curious, and able to thrive in an environment with high autonomy
• Excellent written and verbal communication skills

Preferred

• Research experience in distributed systems, continual learning, or modular neural architectures
• A desire to contribute to open research and collaborate with the broader ML research community

Nice to Have

• Experience at the intersection of cryptography and machine learning

Please note: the benefits listed below apply to full-time employees only

JJanea Systems (USA) is a dynamic team of the best & brightest software engineering specialists and solutions innovators, from around the world. From kernel to cloud, we provide high-impact software development services to Fortune 500 companies.
We are urgently looking an exceptionally talented Senior Machine Learning Engineer to join our rapidly growing consulting team. In this role, you will have the opportunity to work at the cutting edge of the software industry and help work on the client's internal AI/ML practice, as well as utilize your LLM, Data Engineering, ML, and ML Ops skills while working with a team of highly skilled professionals in the AI/ML domain.

• To be considered for this position, you must have the following qualifications:

  • Bachelor's or Master’s degree in Computer Science or a related field
  • 4+ years of experience as a Software Engineer, Platform Engineer, ML Engineer, Data Scientist, AI Engineer, or Data Engineer
  • Flexibility in experience with different programming languages and willingness to adjust to project needs
  • Strong knowledge of Python
  • Knowledge of machine learning algorithms, data pre-processing methods, and ML frameworks (such as PyTorch, TensorFlow, Keras)
  • Experience with containers and Kubernetes in cloud environments (AWS, MS Azure, or GCP)
  • Familiarity with data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo)
  • Understanding of software testing, benchmarking, and continuous integration principles
  • Ability to translate business needs into technical requirements
  • Excellent communication and problem-solving skills, with the ability to break down complex challenges and develop innovative solutions
  • Being self-motivated and adaptable, with the ability to work effectively in fast-paced, dynamic environment
    
    
  Ideal candidates will also have:
  • Familiarity with agent frameworks (such as Langchain, Langgraph, IllamaIndex)
  • Familiarity with developing RAG systems
  • Experience with Natural Language Processing (NLP)
  • Familiarity with monitoring tools (such as DataDog or Langfuse)
  • Any associate cloud certification (AWS preferred)
  Responsibilities:
  • Design scalable data pipelines and infrastructure for enterprise ML systems
  • Implement ML models and systems into production
  • Collaborate with data scientists and software engineers
  • Deploy scalable tools and services for machine learning training and inference
  • Evaluate new technologies to improve ML system performance and reliability
  • Apply software engineering best practices, including CI/CD, to ML development
  • Facilitate the development and deployment of ML proof-of-concepts
  • Review, refactor, optimize, containerize, deploy, version, and monitor ML models
  • Implement monitoring and alerting solutions to ensure the reliability and performance of machine learning systems
  • Optimize and automate the machine learning deployment process to ensure efficiency and reproducibility
  • Collaborate with cross-functional teams to troubleshoot and resolve issues related to machine learning deployments
  • Stay updated with industry trends and apply knowledge to drive innovation
  • Promote industry best practices and enhance team expertise
    
    
  Why join Janea? Because world-class talent deserves world-class opportunities. What we offer:
  • Competitive compensation with benefits, paid vacation, and sick leave.
  • The opportunity to work with a globally diverse team of top engineering talent on the industry’s toughest engineering challenges.
  • Ultra-flexible working conditions – we provide a generous office equipment allowance so you can work from home, we can also provide you with a desk at an office/coworking facility near you, or use both. No business travel necessary.
  • An enjoyable, start-up work environment, with excellent opportunities for professional growth and development.
  • Flexible working hours – as a remote-first company, our focus has always been on getting the job done well, not when or where it gets done.
   

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