Software Engineer - Applied ML - job 1 of 2

As a Software Engineer - Applied ML at Cohere, your responsibilities include collaborating directly with customers to understand their challenges, designing and implementing AI solutions, and engaging with large language models. You will own significant areas of the product, work across the front-end and back-end systems, and manage projects throughout the entire product lifecycle, from conceptualization to production, all while experimenting to enhance product quality and customer engagement.

To qualify for the Software Engineer - Applied ML position at Cohere, candidates should have proficiency in programming languages such as Go, Python, Node, or React. Experience in building machine learning infrastructure, developing language models with frameworks like Lang Chain, and scaling products at hyper-growth companies is essential. Strong communication skills and the ability to travel up to 25% to client sites are also required.

At Cohere, a Software Engineer - Applied ML will work with several advanced tools and technologies, including programming languages like Go, Python, and JavaScript libraries such as React and Next.js. You will also engage with AI frameworks like Lang Chain and the performance evaluation tools associated with language models, such as the Llama Index, allowing you to innovate and create impactful AI solutions.

The Software Engineer - Applied ML role at Cohere contributes to AI adoption in enterprises by addressing and solving critical customer problems through tailored AI solutions. By applying creativity and technical skills to bridge gaps in enterprise AI implementation, you will enable organizations to successfully integrate advanced AI technologies into their operations, potentially transforming industries and driving significant business value.

The work environment at Cohere for a Software Engineer - Applied ML is open, inclusive, and collaborative. You will be part of a passionate team of researchers, engineers, and designers who value diverse perspectives and celebrate innovation. Working in London, you can expect a culture that encourages hard work, creativity, and a shared commitment to making impactful contributions while enjoying flexible work options and excellent employee benefits.

In responding to this question, share an example from your experience where you used machine learning to solve a specific problem. Discuss the objectives of the project, the technologies you utilized, and how your solutions positively impacted the outcome. Highlight your role and the collaboration involved, emphasizing results achieved.

Reflect on specific challenges you have encountered when building ML infrastructure, such as scalability, data integrity, or model performance. Discuss the strategies you employed to tackle these challenges, whether through tool selection, collaboration with cross-functional teams, or innovative coding practices, providing insights into your problem-solving abilities.

To answer, outline your structured approach to problem-solving, which may involve understanding requirements thoroughly, analyzing data, brainstorming solutions, and iterative testing. Providing an example where this approach was applied successfully within a project will illustrate your analytical skills and competency in the Software Engineer role.

Discuss the programming languages you excel in and provide examples of projects where you applied these languages. Explain why they were beneficial for the tasks at hand, illustrating how your skills could be an asset to the Cohere team as a Software Engineer - Applied ML.

Highlight your proactive approach to keeping abreast of AI and ML advancements, such as following relevant blogs, participating in online courses, attending industry conferences, or contributing to open-source projects. Emphasizing a keen interest in continuous learning will resonate well with Cohere's innovative culture.

Explain how diversity in a tech team introduces varied perspectives that contribute to more innovative problem-solving and product development. Share any personal experiences where diverse collaboration led to enhanced results, demonstrating your understanding of working in inclusive environments.

In your answer, recall a specific instance where you successfully explained a complex concept. Discuss your approach to simplifying the information and how it enhanced understanding or facilitated decision-making for the audience.

Share the tools and metrics you commonly use for evaluating machine learning models, such as accuracy, precision, recall, or F1 score. Discuss your rationale for selecting these tools and how they have contributed to improving model performance.

Describe your method for prioritizing tasks, highlighting how you evaluate urgency versus importance. Share an example of a challenging project where effective prioritization helped you meet deadlines while ensuring quality outcomes.

In your response, express genuine enthusiasm for Cohere’s mission and the opportunity to contribute to innovative AI projects. Connect your skills, experiences, and career goals to the role, showcasing how they align with the company’s vision and culture.