Machine Learning Data Scientist – Research Translation & Prototyping

Blueprint Technologies•Redmond, WA

8h•$145,000 - $155,000•Remote

About The Position

As a Machine Learning Data Scientist – Research Translation & Prototyping, you will work at the intersection of cutting-edge AI research and real-world application. You will partner with researchers, engineers, designers, and product stakeholders to evaluate emerging technologies, build rapid prototypes, and develop machine learning solutions that help transform experimental concepts into usable tools and experiences. This role is ideal for a hands-on builder who thrives in fast-paced, ambiguous environments and enjoys translating research, data, and novel ideas into measurable outcomes. Success requires strong technical expertise in machine learning and software engineering, the ability to design and execute experiments, and a passion for quickly validating new technologies through prototyping and evaluation.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, Data Science, Mathematics, Statistics, or a related technical field.
5–7+ years of professional experience in machine learning, data science, applied AI, software engineering, or a related discipline.
Strong experience developing machine learning models and AI-powered solutions.
Demonstrated experience with data science methodologies, experimentation, model evaluation, and statistical analysis.
Hands-on software engineering experience, including coding, debugging, testing, and deployment.
Experience building data-intensive applications, machine learning systems, experimentation platforms, or AI-enabled products.
Strong programming skills and the ability to diagnose and resolve technical issues.
Experience evaluating, improving, and maintaining machine learning models, data pipelines, and AI applications.
Ability to quickly learn new technologies, adapt to changing priorities, and contribute effectively in ambiguous, fast-moving environments.
Strong communication skills with the ability to explain technical concepts and findings to both technical and non-technical audiences.
Experience working collaboratively across research, engineering, product, and business teams.

Nice To Haves

Experience translating research concepts, academic publications, or emerging technologies into working prototypes and production-ready solutions.
Experience with foundation models, large language models (LLMs), generative AI systems, multimodal AI, agentic workflows, and retrieval-augmented generation (RAG).
Proven ability to rapidly prototype and iterate on ideas using modern AI development tools and AI-assisted coding workflows.
Experience designing evaluation frameworks, benchmarks, and success metrics for AI systems.
Familiarity with model fine-tuning, experimentation, model validation, and performance optimization techniques.
Experience working on research-driven initiatives or innovation-focused environments.
Ability to ramp up quickly on new projects and deliver meaningful results within short timelines.
Experience supporting end-to-end machine learning solution development, from experimentation through deployment and validation.
Demonstrated flexibility and success working across multiple research or product domains simultaneously.
Availability for a long-term engagement (12+ months preferred).

Responsibilities

Collaborate with research, engineering, and cross-functional teams to evaluate emerging AI and machine learning technologies and determine their practical value.
Design, develop, and implement machine learning models, AI-powered applications, and experimental systems.
Build rapid prototypes and proof-of-concept solutions to validate new technologies and research concepts.
Fine-tune, benchmark, validate, and improve machine learning models using real-world datasets.
Develop evaluation frameworks, benchmarks, and success metrics for AI systems, foundation models, generative AI solutions, multimodal experiences, and agent-based workflows.
Design and execute quantitative and qualitative experiments to assess model performance, user engagement, technology adoption, and overall effectiveness.
Analyze system requirements, document technical specifications, and develop software solutions aligned with project objectives.
Gather, process, and analyze data to generate actionable insights and support decision-making.
Evaluate, troubleshoot, and improve machine learning pipelines, AI systems, and software implementations.
Develop, test, and maintain software applications and supporting infrastructure.
Create and execute test plans, perform unit testing, and support quality assurance efforts.
Support deployment, validation, and post-implementation monitoring of solutions, resolving issues identified during testing and rollout.
Stay current with advancements in machine learning, generative AI, multimodal systems, agentic workflows, and related research areas to identify opportunities for innovation and application.