Applied Scientist Alexa Smart Home jobs at Amazon in Canada, Vancouver

In this role, you will take a pragmatic and iterative approach to building software: You will simplify and get things done while experimenting on rapid iterations on the new concepts. You will need to be a focused and thoughtful leader of your team who works effectively with external and internal partners.Key job responsibilities
As a SDE, you will be responsible for designing, developing, testing, and deploying solutions for Alexa Smart Properties and enterprise customers world-wide customer. In this, you will collaborate closely with Alexa/AWS/Solution Architects and many other teams and functions to influence our overall strategy and define the team’s road map. You will also drive the system architecture, spearhead best practices that enable a quality product, and help coach and develop junior engineers. A successful candidate will have an established background in engineering large scale software systems, a strong technical ability, great communication skills, and a motivation to achieve results in a fast paced environment.A day in the life- Design and develop micro-services to create new core software services.- Build service architectures that have rock solid availability and performance.
We work on expanding Alexa's use case by creating features and core Alexa system functionality that allows Alexa to be use by business enterprises (as opposed to personal homes). You will be writing code to deliver these pieces of functionality.

- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience programming with at least one software programming language

- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent

As an Sr. Applied Scientist, you'll work alongside domain experts, engineers, and other scientists to understand business problems, propose scientific solutions, and deploy them to production. You'll work on scientific initiatives for accelerating reconciliation, standardization, and onboarding. This includes:
- Leveraging GenAI/LLMs to build agentic solutions to accelerate accounting-related research/tasks and produce proactive insights.
- Building AI trust and safety in the financial domain.
- Establishing scalable, efficient, automated processes for large-scale data analysis, machine learning model development, model validation, and serving.
- Developing training/evaluation datasets for model fine-tuning.
You will need to have a start-up like mindset, as you will be working an in a highly iterative and collaborative environment with SDEs, Product Managers, and Accounting stakeholders to propose ideas, experiment, and scale rapidly. You should have a keen eye for what a good user experience should look like, possess excellent written and verbal communication, and have a keen interest in learning about accounting and financial processes.

- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning

- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.

A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems.

- Are enrolled in a PhD
- Are 18 years of age or older
- Work 40 hours/week minimum and commit to 12 week internship maximum
- Can relocate to where the internship is based
- Experience programming or scripting language like Python, Java, C or C++

- Have publications at top-tier peer-reviewed conferences or journals
- Are enrolled in a academic program that is physically located in Canada

As an Applied Scientist, you'll work alongside domain experts, engineers, and other scientists to understand business problems, propose scientific solutions, and deploy them to production. You'll work on scientific initiatives for accelerating reconciliation, standardization, and onboarding. This includes:
- Leveraging GenAI/LLMs to build agentic solutions to accelerate accounting-related research/tasks and produce proactive insights.
- Building AI trust and safety in the financial domain.
- Establishing scalable, efficient, automated processes for large-scale data analysis, machine learning model development, model validation, and serving.
- Developing training/evaluation datasets for model fine-tuning.
You will need to have a start-up like mindset, as you will be working an in a highly iterative and collaborative environment with SDEs, Product Managers, and Accounting stakeholders to propose ideas, experiment, and scale rapidly. You should have a keen eye for what a good user experience should look like, possess excellent written and verbal communication, and have a keen interest in learning about accounting and financial processes.

- 3+ years of solving business problems through machine learning, data mining and statistical algorithms experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- 3+ years of programming in Java, C++, Python or related language experience
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

- Experience applying theoretical models in an applied environment

As an Applied Scientist for Promise Optimization, you will spearhead the development and productionization of the latest machine learning models, addressing critical predictive and forecasting challenges. Your role will be pivotal in scaling, automating, and deploying these models, collaborating with a diverse scientific team that includes software engineers, economists, data engineers, and fellow applied scientists.This position is ideal for a forward-thinking scientist eager to apply the latest breakthroughs in AI and machine learning to solve complex, high-impact problems. Throughout your projects, you'll need to strike a delicate balance between analytical rigor and pragmatism, always prioritizing the delivery of tangible value in response to pressing business questions. You'll have the opportunity to shape the future of our organization through the innovative use of the latests technologies and methodologies.Responsibilities include:- Ensuring production models are robust, scalable, and effectively address both business needs and software engineering requirements.
- Leveraging big data and AWS technologies to scale, automate, and productionize core statistical models, enabling rapid deployment of refreshed models.
- Mentoring other researchers on the effective use of these cutting-edge tools.

- 3+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

- Experience in professional software development

In this role, you will have ownership of the end-to-end development of solutions to complex problems and you will play an integral role in strategic decision-making. You will also work closely with engineers, operations teams, product owners to build ML pipelines, platforms and solutions that solve problems of defect detection, automation, and workforce optimization.

- 3+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

As a Senior Applied Scientist in the team, you will play a critical role in driving the development of conversational assistants, in particular those based on Large Language Models (LLM's), that meet enterprise standards. You will handle Amazon-scale use cases with significant impact on our customers' experiences.Key job responsibilities- You will work on core LLM technologies, including developing best-in-class modeling, prompt optimization algorithms to enable Conversation AI use cases
- Perform model/data analysis and monitor metrics through online A/B testing

- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning

- Solid Machine Learning background and familiar with SOTA machine learning techniques