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Blue Origin · 2 weeks ago

Applied Machine Learning Scientist II (AI/ML)

Greater Seattle Area

Full-time

Onsite

Mid Level

$121K/yr - $170K/yr

3+ years exp

Blue Origin is a company focused on developing reusable, safe, and low-cost space vehicles and systems. The Applied Machine Learning Scientist II will work within the Enterprise Technology group to develop innovative AI models that enhance manufacturing processes for space crafts, contributing to the efficiency and velocity of launches.

AerospaceManufacturingNational SecurityRenewable Energy

No H1B

U.S. Citizen Only

Responsibilities

Design experiments, hypotheses and rigorously benchmark solutions

Conduct literature review to identify relevant datasets, models and tools to prototype and improve AI systems in production

Develop techniques for data preprocessing, extraction, and synthetic generation

Deploy algorithms by creating docker images, spinning up containers, creating endpoints and microservices

Document your findings and present to stakeholders

Qualification

Machine LearningPythonDeep LearningAI frameworksStatistical analysisData preprocessingCloud platformsContainer orchestrationDistributed SystemsSoft skills

Required

Machine Learning

Masters or PhD with relevant course work - Linear algebra, Statistics, NLP, and Computer Vision

3+ years of experience in the industry working on real-world data and building AI systems

4+ years of experience coding - Python and AI frameworks (TensorFlow/ PyTorch)

Background in Deep Learning - vision-language models (VLMs), transformers, training LoRA adapters, model distillation, and fine-tuning

Strong mathematical foundations - linear algebra, calculus, statistics

Ability to curate large-scale multimodal datasets - data preprocessing and generation of datasets (synthetic and human-labeled)

Experience with ML libraries and tools – pandas, torchvision, NLTK, spaCy, diffusers, vllm, transformers, DSPy, Ray, LangGraph, DeepSpeed etc.)

Designing machine learning experiments – understanding and improving model behaviors and proposing solutions for generalization/interpretability

Familiarity with reinforcement learning principles

Understanding of AI ethics and responsible AI development practices

Distributed Systems

Expertise with GPU optimization and Cloud platforms (AWS, Azure)

Experience with container orchestration tools (Docker, Kubernetes)

CI/CD pipeline implementation and management

Systems architecture design for AI applications

Preferred

Prompt and context engineering - experience with implementation of Retrieval-Augmented Generation (RAG)

Proficiency in generative model architectures - GANs, VAEs, Diffusion Models

Experience with post-training techniques - instruction tuning, preference modeling, or RLHF

Experience with 3D modeling and building multi-agentic workflows

Demonstrate concrete achievements from ML projects that involve building multimodal algorithms for some of the following tasks: segmentation, object detection, classification, question and answering and so on

Interest or experience in test-time/inference scaling

Research publications in machine learning conferences, journals and/or open-source contributions