Applied Machine Learning Engineer
About Us:
Here at Fireworks, we’re building the future of generative AI infrastructure. Fireworks offers the generative AI platform with the highest-quality models and the fastest, most scalable inference. We’ve been independently benchmarked to have the fastest LLM inference and have been getting great traction with innovative research projects, like our own function calling and multi-modal models. Fireworks is funded by top investors, like Benchmark and Sequoia, and we’re an ambitious, fun team composed primarily of veterans from Pytorch and Google Vertex AI.
Job Overview
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.
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.
Compensation is determined by various factors including individual qualifications, experience, skills, interview performance, market data, and work location. The listed salary range for this role is a guideline and may be modified.
Compensation is determined by various factors including individual qualifications, experience, skills, interview performance, market data, and work location. The listed salary range for this role is a guideline and may be modified.
Why Fireworks AI?
- Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.
- Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally.
- Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results.
- Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation.
Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.
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Fireworks AI
Fireworks.ai provides a generative AI platform as a service, focusing on rapid product iteration and cost-efficiency.
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