Applied Machine Learning Engineer, Singapore

What matters most

• Customer deployment

  Work with Account Executives and Solutions Architects to integrate and deploy ML solutions for customers. The role calls for leading complex technical projects with a focus on customer success.

• Proofs of concept and applications

  Build and present PoCs, then design, develop, and deploy end-to-end AI applications tailored to customer needs.

• Models in production

  Develop, fine-tune, and operationalize models that deliver business value and improve user experiences. Hands-on SFT and RLHF or RFT experience is preferred, not required.

• Platform and model enablement

  Add features and resolve issues in the internal ML platform, and enable new models in the platform or customer environments.

• Performance at scale

  Improve the performance, efficiency, and scalability of deployed models and applications; the posting also highlights low-latency inference and scalable model serving.

Likely challenges to think through

• Turning a PoC into a deployed application

  Likely preparation challenge, not a confirmed employer problem: a compelling demo may need different engineering decisions from a customer deployment. Prepare to explain how you would define a PoC's scope, validate its value, and identify what must change before deployment.

• Balancing customer needs with platform work

  Likely preparation challenge: tailored applications and customer-environment integrations may compete with reusable platform features and bug fixes. Prepare a framework for deciding what belongs in the shared platform versus a customer-specific implementation.

• Trading off model quality and serving performance

  Likely preparation challenge: fine-tuning and new-model enablement must coexist with efficiency, scalability, and low-latency serving goals. Prepare to describe how you would measure application quality alongside deployment performance and choose among trade-offs.

Topics to prepare

• Generative AI and model selection

  Review how to choose and evaluate a model for a customer use case; compare two hypothetical options on output quality, latency and fit. The posting prefers understanding of generative AI and ML principles, and describes text, image, embedding, audio and multimodal workloads.

• Fine-tuning approaches

  Explain when supervised fine-tuning (SFT) versus RLHF or RFT might be appropriate, including the feedback and evaluation each would need. Hands-on experience with these approaches is preferred, not a minimum qualification.

• Model serving trade-offs

  Work through a serving design and explain how latency, efficiency and scalability affect one another. Improving deployed performance is a responsibility; the page also highlights low-latency inference and scalable model serving.

• Enterprise integration and customer success

  Sketch the decisions involved in taking a customer need from integration through deployment, including how to define success with Account Executives and Solutions Architects. The role explicitly combines customer-facing delivery with enterprise infrastructure knowledge, which is preferred.

• Technical project leadership

  Outline how you would sequence a complex customer-focused project, handle a change in requirements and communicate a trade-off to engineering and customer-facing colleagues. Leading such projects and working across teams are minimum qualifications.

Skills to revise

• Code a platform change

  Practice implementing a small ML-platform feature or bug fix, with a focused test and a clear explanation of the change. The role includes platform features and fixes; robust coding is required, while Python proficiency is preferred.

• Build an end-to-end AI application

  As a preparation exercise, design and implement a small application around a defined customer need, then explain how you would deploy it. Designing, developing and deploying customer-tailored AI applications is an explicit responsibility.

• Present a proof of concept

  Prepare a short demonstration with a stated customer problem, observable result and limitations; rehearse explaining it to both technical and customer-facing colleagues. Building and presenting PoCs is an explicit responsibility.

• Enable a new model

  Practice planning and testing the steps to integrate a model into an existing application or platform, including how you would detect a failed integration. Enabling new models in the platform or customer environments is a stated responsibility; this exercise does not assume a particular tool.

• Measure and improve deployed performance

  Define a baseline for latency and efficiency, propose one change, and compare the results while checking that application behavior still meets the need. The posting explicitly calls for improving performance, efficiency and scalability of deployed models and applications.

• Map evidence to the minimum qualifications

  Prepare a one-page record of relevant education, software engineering experience, coding work, and complex projects led. The posting requires a related bachelor’s degree and 5+ years in software engineering; Python and customer-facing experience are preferred.

• Rehearse a customer-focused project walkthrough

  If you have a relevant example, outline the customer need, your technical decisions, collaborators, deployment, and measured outcome. Otherwise, prepare a clearly hypothetical scenario. The role involves customer success and working with Account Executives and Solutions Architects.

• Prepare a proof-of-concept presentation

  Sketch a short AI demo with a customer use case, what it shows, how you would evaluate it, and what remains before deployment. Building and presenting PoCs is an advertised responsibility; the demo is a preparation exercise, not a stated interview requirement.

• Practice an end-to-end application design

  Draft a diagram covering model choice, application integration, deployment, and evaluation for a hypothetical customer need. Be ready to explain trade-offs for scalability and enterprise infrastructure without assuming Fireworks uses a particular architecture.

• Explain a model-enablement or platform change

  Prepare a technical walkthrough, using a real example if available, of integrating a new model or diagnosing a platform issue. Include tests, rollout considerations, and how you would identify regressions; both model enablement and platform fixes appear in the role.

• Quantify a performance improvement

  Bring before-and-after measurements from relevant work if available, or outline how you would measure latency, efficiency, and scalability. Explain the trade-offs you would investigate for deployed models and applications.

• Review fine-tuning decisions

  Prepare a concise comparison of supervised fine-tuning and RLHF or RFT, including when each might be appropriate and how you would evaluate results. Hands-on experience with these methods is preferred, not listed as a minimum qualification.

• Draft questions for the employer

  Prepare questions about how customer PoCs become deployed applications, how platform and customer-environment work is divided, and how engineers collaborate with partners and the GTM team. These topics come from the advertised responsibilities.