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Requirement - AI Engineer - Machine Learning 3

Location- Redmond, WA 98052-Remote

Contract W2

Title: Machine Learning Data Scientist – Research Translation & Prototypin

Top 3 Must-Have HARD Skills & years of experience for each: 

1. Machine Learning & Applied AI Development (5-7 years)

2. Data Science, Experimentation & Model Evaluation (5-7 years)

3. Software Engineering & Rapid Prototyping (5-7 years)

Best vs. Average: The ideal resume would contain.

→ Demonstrates strong flexibility

→ Ability to rapidly ramp on new projects (1–3 days), and deliver results quickly (within ~5 days)

→ Has hands-on experience with AI-assisted coding and rapid prototyping

→ Bachelor's degree in a technical field such as computer science, computer engineering or related field required

Summary:

• As a Machine Learning Data Scientist, you will collaborate closely with researchers, engineers, designers, and product partners to evaluate emerging AI technologies, build rapid prototypes, and develop novel machine learning solutions that make advanced research understandable, usable, and testable. You will design experiments, create evaluation frameworks, fine-tune and validate models, and help identify which technologies warrant broader investment and adoption.

• This role is ideal for a technically strong builder who enjoys ambiguity, learns quickly, and can move fluidly between research papers, datasets, prototypes, and production-scale systems. Success requires scientific rigor, strong product judgment, and a passion for turning breakthrough ideas into tools, workflows, and experiences that empower researchers, developers, and customers.

• This role is ideal for a technically strong builder who enjoys ambiguity, learns quickly, and can move fluidly between research papers, datasets, prototypes, and production-scale systems. Success requires scientific rigor, strong product judgment, and a passion for turning breakthrough ideas into tools, workflows, and experiences that empower researchers, developers, and customers.

• Candidates should be prepared to discuss projects that demonstrate the ability to translate research, emerging technology, or novel ideas into working prototypes, experiments, or deployed solutions.

Job Responsibilities:

• Fine-tune and improve a variety of sophisticated software implementation projects

• Gather and analyze system requirements, document specifications, and develop software solutions to meet client needs and data

• Analyze and review enhancement requests and specifications

• Implement system software and customize to client requirements

• Prepare the detailed software specifications and test plans

• Code new programs to client’s specifications and create test data for testing

• Modify existing programs to new standards and conduct unit testing of developed programs

• Create migration packages for system testing, user testing, and implementation

• Provide quality assurance reviews

• Perform post-implementation validation of software and resolve any bugs found during testing

Additional Responsibilities:

• Collaborate with client Research teams to evaluate, adapt, and operationalize emerging AI and machine learning innovations into functional prototypes and experimental systems.

• Design and execute quantitative and qualitative experiments that measure model performance, user engagement, research impact, and technology adoption.

• Develop evaluation frameworks, benchmarks, and success metrics for foundation models, generative AI systems, multimodal experiences, and agent-based workflows.

• Fine-tune, validate, and benchmark machine learning models using real-world datasets and emerging research techniques.

• Build rapid prototypes and proof-of-concepts that help researchers, partners, and stakeholders assess the practical value of new technologies.

• Stay current with advances in machine learning, generative AI, agentic systems, multimodal models, and evaluation methodologies, identifying opportunities to apply new capabilities across client Research.

Qualifications:

• Bachelor's degree in a technical field such as computer science, computer engineering or related field required

• 5-7 years’ experience required

• Strong technical foundations in software engineering, machine learning, statistics, and experimental design.

• Experience building data-intensive applications, machine learning systems, experimentation platforms, or AI-powered products.

• Experience evaluating, debugging, and improving machine learning models, data pipelines, and AI-powered applications.

• Experience in programming and experience with problem diagnosis and resolution

• Ability to thrive in ambiguous, rapidly changing environments where requirements evolve through experimentation and discovery.

• Experience with foundation models, generative AI systems, multimodal models, agentic workflows, retrieval-augmented generation (RAG), or related AI technologies.

Additional Information  

Explain a typical day in the role.: 

No two days look exactly alike. One week you might be evaluating a new foundation model, the next building a prototype with researchers, and the following week presenting findings that influence product, research, or investment decisions.

What is the ideal background of a candidate for this role?

The ideal candidate has experience in machine learning, data science, or applied AI, with a demonstrated ability to translate emerging research into practical prototypes, experiments, and insights. They should be comfortable working in ambiguous, fast-moving environments, designing evaluations, analyzing data, collaborating across disciplines, and communicating technical findings to diverse audiences. Experience with foundation models, generative AI, research-driven development, and rapid prototyping is highly desirable.

 What are the unique selling points that would get candidates interested in your role over another?

This role sits at the intersection of client Research and applied AI innovation. Candidates will work directly with cutting-edge research, helping transform breakthrough ideas into prototypes, experiments, and technologies that influence future client products and experiences. The position offers unusual breadth, allowing individuals to work across multiple AI domains, collaborate with leading researchers, contribute to publications and patents, and operate in a small, highly autonomous team where creativity, experimentation, and technical excellence are equally valued.

How will contractor performance be measured?

Performance will be measured through successful delivery of prototypes, experiments, and AI/ML solutions; the quality of technical contributions; the ability to generate actionable insights through data and experimentation; collaboration with cross-functional teams; and the overall impact of the work on research validation, technology adoption, and strategic decision-making.