Member of Technical Staff, Applied Machine Learning Engineer, Singapore
Join Fireworks AI as an Applied Machine Learning Engineer to bridge AI research and real-world applications in a fast-growing team.
Posted by employer 3 months ago
First seen on Joblaze 3 months ago
Last verified on the company career page 1 hour ago
AI in the day-to-day
Your work will focus on developing, fine-tuning, and operationalizing machine learning models that drive business value.
Requirements
- Experience
- 5+ years
- Education
- Bachelor's degree
Not disclosed in this posting: compensation, work arrangement, visa sponsorship.
Joblaze summary
In the role of Applied Machine Learning Engineer at Fireworks AI, the individual will focus on developing and operationalizing machine learning models that enhance user experiences and drive business value. Key skills include robust coding abilities, particularly in Python, and experience in customer-facing engineering roles, ideally with a background in generative AI and machine learning principles. This position is well-suited for seasoned professionals who thrive in dynamic environments and are adept at leading complex technical projects. Fireworks AI offers a collaborative atmosphere where innovative solutions are prioritized, reflecting its commitment to cutting-edge AI infrastructure.
Joblaze insights
- Listed about 3 months ago — first seen on Joblaze July 23, 2026. Last confirmed on Fireworks AI's careers page October 28, 2026.
Quick facts
- How much experience is required?
- At least 5 years of relevant experience for this Member of Technical Staff, Applied Machine Learning Engineer, Singapore role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: AI/ML, Python.
- What seniority level is this role?
- Fireworks AI targets senior candidates for this position.
- Is this full-time or contract?
- Full-time for this Member of Technical Staff, Applied Machine Learning Engineer, Singapore role at Fireworks AI.
From the original posting
The Role:
As an Applied Machine Learning Engineer, you will serve as a vital bridge between cutting-edge AI research and practical, real-world applications. Your work will focus on developing, fine-tuning, and operationalizing machine learning models that drive business value and enhance user experiences. This is a hands-on engineering role that combines deep technical expertise with a strong customer focus to deliver scalable AI solutions.
Key Responsibilities:
Customer Success: Collaborate directly with the GTM team (Account Executives and Solutions Architects) to ensure smooth integration and successful deployment of ML solutions.
Demo / Proof of Concept (PoC): Build and present compelling PoCs that demonstrate the capabilities of our AI technology.
Application Build: Design, develop, and deploy end-to-end AI-powered applications tailored to customer needs.
Platform Features / Bug Fixes: Contribute to the internal ML platform, including adding features and resolving issues.
New Model Enablements: Integrate and enable new machine learning models into the existing platform or client environments.
Performance Optimizations: Improve system performance, efficiency, and scalability of deployed models and applications.
Partnership Enablement: Work closely with partners to enable joint AI solutions and ensure seamless collaboration.
Minimum Qualifications:
Bachelor’s degree in Computer Science, Engineering, or a related technical field.
5+ years of experience in a software engineering role, with a strong preference for customer-facing roles.
Robust coding skills required, preferably with proficiency in Python.
Demonstrated ability to lead and execute complex technical projects with a focus on customer success.
Strong interpersonal and communication skills; ability to thrive in dynamic, cross-functional teams.
Preferred Qualifications:
Master’s degree in Computer Science, Engineering, or a related technical field.
Experience working in a startup or fast-paced environment.
Hands-on experience fine-tuning machine learning models, including supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF or RFT).
Solid understanding of generative AI, machine learning principles, and enterprise infrastructure.
Why Fireworks?
Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.
Standard company text repeated across Fireworks AI's postings is omitted here.