The Role:

As a Software Engineer on our Cloud Infrastructure team, you'll be at the forefront, architecting and building the foundational systems that power Fireworks AI's revolutionary generative AI platform. You'll spearhead the creation of one of the world's first virtual clouds, seamlessly serving AI workloads across the globe and every cloud provider. Your mission: to deliver unparalleled reliability, efficiency, and scalability, fueling the world's most innovative AI products.This is a highly technical role requiring deep expertise in distributed systems, cloud-native infrastructure, and machine learning platforms. You’ll partner closely with engineering partners, product teams, and infrastructure stakeholders to design solutions that balance performance, cost-efficiency, and operational simplicity across compute, storage, and networking layers.

Key Responsibilities:

• Architect and build scalable, resilient, and high-performance backend infrastructure to support distributed training, inference, and data processing pipelines.
• Lead technical design discussions, mentor other engineers, and establish best practices for building and operating large-scale ML infrastructure.
• Design and implement core backend services (e.g., job schedulers, resource managers, autoscalers, model serving layers) with a focus on efficiency and low latency.
• Drive infrastructure optimization initiatives, including compute cost reduction, storage lifecycle management, and network performance tuning.
• Collaborate cross-functionally with ML, DevOps, and product teams to translate research and product needs into robust infrastructure solutions.
• Continuously evaluate and integrate cloud-native and open-source technologies (e.g., Kubernetes, Kubeflow, MLFlow) to enhance our platform’s capabilities and reliability.
• Own end-to-end systems from design to deployment and observability, with a strong emphasis on reliability, fault tolerance, and operational excellence.

Minimum qualifications:

• Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience).
• 5+ years of experience designing and building backend infrastructure in cloud environments (e.g., AWS, GCP, Azure).
• Proven experience in ML infrastructure and tooling (e.g., PyTorch, TensorFlow, Vertex AI, SageMaker, Kubernetes, etc.).
• Strong software development skills in languages like Python, or C++.
• Deep understanding of distributed systems fundamentals: scheduling, orchestration, storage, networking, and compute optimization.

Preferred qualifications:

• Master’s or PhD in Computer Science or related field.
• Experience leading infrastructure projects supporting large-scale ML/AI workloads or high-throughput systems.
• Familiarity with infrastructure-as-code and CI/CD tooling (e.g., Terraform, ArgoCD, GitOps).
• Track record of driving system performance, reliability, and cost-efficiency improvements.
• Contributions to open-source cloud or ML infrastructure projects a plus.

About the Role

Fireworks is building the fastest and most scalable generative AI platform in the world. We help leading startups and enterprises deploy open-source and custom LLMs in production with best-in-class performance, latency, and cost efficiency. Backed by Sequoia, Lightspeed, and Benchmark, Fireworks is trusted by GenAI-native companies like Cursor, Vercel, Genspark,  and others across healthcare, finance, and developer tooling. We’re past $300M+ ARR and growing fast. Join us to define how GenAI gets adopted in the real world.

We’re hiring an AI Native Account Executive to drive adoption of Fireworks within the most ambitious GenAI startups. You’ll engage deeply with technical founders, ML leads, and product teams — helping them understand how Fireworks fits into their stack and guiding them through evaluation, onboarding, and scale-up. This isn’t a traditional transactional sales role: we’re looking for someone who can speak credibly to technical teams, identify high-impact use cases, and relentlessly pursue value creation for the customer. You’ll work cross-functionally with GTM engineers, product, and applied ML to unblock adoption, accelerate time to value, and grow usage in your accounts.

Responsibilities

• Own a portfolio of GenAI-native startup accounts and drive adoption across key use cases
• Actively identify, engage, and qualify new high-potential prospects through warm outreach, events, and network-driven channels
• Lead technical and product conversations with founders, ML engineers, and infra leads
• Help customers get live quickly by coordinating onboarding, benchmarking, and integration efforts
• Work closely with internal teams to shape tailored solutions, POCs, and fine-tuning approaches
• Translate customer feedback into actionable insights for the product and engineering teams
• Track account health and usage data to identify expansion opportunities
• Be a visible representative of Fireworks in the GenAI ecosystem — including attending demo days, meetups, and online communities

You Might Be a Fit If You

• Have 3+ years of experience in sales, business development, customer success, or product roles focused on technical buyers
• Thrive in fast-moving environments and love engaging with startup founders and builders
• Can understand and explain technical topics like open source  inference, API performance, model tuning, or GPU architecture
• Are proactive, persistent, and creative in breaking into accounts and building relationships
• Have strong communication skills and can earn trust with both engineers and executives
• Want to be on the front lines of the GenAI movement and work closely with companies shaping the future

Why Fireworks

• Work directly with some of the most innovative GenAI startups in the world
• Join a lean, high-impact GTM team where your work directly drives company growth
• Build lasting relationships and help startups get real value from powerful infrastructure
• Collaborate with world-class engineers and operators building at the bleeding edge of AI
• Competitive base salary, strong equity, and long-term upside

In the last few months alone we've launched the Fireworks Training Platform, partnered with Microsoft Azure Foundry, and published research straight from our production systems which is helping scale some of the most innovative companies and products of our generation.

As an SA you'll be close to all of it. The customer conversations you lead directly feed our roadmap, and the work you do shows up in what we build and publish next. A few examples of what that looks like in practice:

• Frontier RL is cheaper than the mega-cluster narrative suggests — we ran cross-region rollouts using 98% sparse weight deltas and published what we learned
• Training-inference parity in MoE models — kernel fusions that are mathematically equivalent can still drift numerically; we shipped the fixes across Kimi K2.5 and Qwen3.5-MoE
• The fine-tuning bottleneck isn't the algorithm — integration friction and iteration speed are what actually stall teams; we documented the patterns across dozens of customer engagements

If you want to work on hard infrastructure problems, be close to the customers pushing the frontier, and actually see your work ship come work with us!

The Role:

Solutions Architects at Fireworks are the technical and strategic owners of the customer relationship from the first discovery call through to production. You'll work with some of the most ambitious engineering teams in the world, translating complex business problems into concrete AI solutions built on the Fireworks platform.

This is a role that demands both technical depth and strong people skills. You'll need to earn the trust of ML engineers and VPs in the same meeting, scope and execute POCs without losing sight of the customer's definition of success, and know enough about inference, fine-tuning, and model architecture to make credible recommendations under pressure.

We hire SAs across two tracks. Both require strong technical grounding and sharp customer instincts; the difference is where each track places its emphasis.

Enterprise SA Track

• Works with digital native and large organizations — navigating multiple stakeholders, procurement cycles, and executive relationships
• Heavy emphasis on executive presence: equally comfortable presenting to a CTO and debugging a latency issue with an ML engineer
• Leads complex technical sales: discovery, solution design, POC execution, commercial negotiation
• Owns the account relationship end-to-end, including expansion and renewal
• Strong commercial instincts understands how to build a business case and close large deals

Applied AI Track

• Works with high-velocity accounts and technology partners startups, ISVs, and hyperscaler ecosystems
• Heavier emphasis on technical execution more time in the code, building integrations, running enablements
• Faster iteration cycles with less org navigation focused on shipping working solutions quickly
• Embeds with partner engineering teams to enable their AI practices and build joint solutions
• Comfortable operating across engineering, partnerships, and sales simultaneously

What You'll Work On:

Regardless of track, SAs at Fireworks own a consistent set of responsibilities:

Technical Discovery & Solution Design

• Lead structured discovery conversations to unpack customer pain points, constraints, and success criteria before proposing solutions
• Design end-to-end architectures for GenAI applications covering model selection, inference configuration, RAG design, and fine-tuning strategy

POC Scoping & Execution

• Define what a minimal, compelling proof-of-concept looks like and own it through to delivery. Prioritize and stack rank opportunities: manage scope creep, set realistic timelines, and keep the customer aligned on what success looks like
• Work alongside product and engineering teams to execute technically rigorous POCs

Performance Engineering

• Run inference sweeps and establish performance baselines for customer workloads
• Create and configure deployments tuned to specific latency, throughput, and cost targets

Fine-Tuning & Model Recommendations

• Guide customers on fine-tuning strategy and model recommendations: when to use SFT, DPO, or RFT, and which model family fits their use case
• Build and run fine-tuning pipelines directly for customers
• Evaluate model quality and help customers build robust eval pipelines

Account Ownership & Stakeholder Management

• Own the technical relationship across the account: from champion to executive sponsor
• Navigate complex organizations, build trust at multiple levels, and maintain momentum through long sales cycles
• Feed customer signal: deployment patterns, pain points, feature gaps — back into the product roadmap

What We're Looking For

• 5+ years in a technical, customer-facing role — Solutions Architect, Sales Engineer, Forward Deployed Engineer, Customer facing AI Engineer / Data Scientist or equivalent
• Hands-on experience with the LLM stack: inference trade-offs, fine-tuning methodologies (SFT, RFT, DPO), and deploying models at scale
• Strong Python skills: comfortable reading, writing, and debugging production code
• Exceptional communication: able to run a sharp discovery call, present to a VP, and explain reinforcement learning to an ML engineer in the same afternoon
• Experience with cloud infrastructure (AWS, Azure, GCP) and model serving at scale

The Role

We're looking for a strategic and hands-on Senior GTM Recruiter to join our growing People team and help fuel our next phase of growth. In this role, you'll own full-cycle recruiting for Go-To-Market functions — including Sales, Marketing and Partnerships — partnering closely with GTM leaders to build world-class teams that can take Fireworks' platform to market.

This isn't just a recruiting role, it's a chance to shape how an AI-native company hires. We're looking for someone who is genuinely excited about the AI industry, eager to experiment with AI-powered recruiting tools and workflows, and ready to help define what modern, intelligent talent acquisition looks like. If you love being at the forefront of both AI and hiring, this role was built for you.

Key Responsibilities

• Own full-cycle GTM recruitment across Sales, Marketing, Partnerships, Solutions Engineering, and Customer Success, with a focus on quality, speed, and candidate experience.
• Partner with GTM hiring managers and executives to understand business needs, calibrate on candidate profiles, and develop targeted hiring strategies. Build and maintain talent pipelines and market maps for current and future needs.
• Proactively source and engage high-caliber GTM talent through direct outreach, networking, referrals, and AI-powered sourcing platforms.
• Collaborate with HRBPs, Compensation, and other People functions to align on role design, comp benchmarking, and market intelligence to inform hiring plans.
• Champion AI-native recruiting practices, from sourcing automation and outreach personalization to pipeline analytics, while optimizing recruiting operations and tooling for a scalable, inclusive process.

Minimum Qualifications

• 5+ years of hands-on recruiting experience, ideally in fast-paced, high-growth environments.
• Proven track record recruiting for GTM roles across Sales, Marketing and Partnerships.
• Demonstrated ability to partner with founders, hiring managers, and senior GTM leaders to shape and influence hiring strategy.
• Strong sourcing and candidate engagement skills, with a knack for identifying and activating top GTM talent.
• Ability to work with urgency while maintaining high quality standards and a thoughtful candidate experience.

Preferred Qualifications

• Experience scaling GTM teams at a startup or hypergrowth company, ideally in AI, infrastructure, or developer tools.
• Familiarity with compensation structures and equity benchmarking in early-stage startups.
• Hands-on experience with AI recruiting tools, whether that's AI-assisted sourcing, automated outreach, or workflow automation, and genuine enthusiasm to explore what's next.
• Experience with Talent Operations to implement and optimize recruiting tools, systems, and workflows.

Who You Are (Beyond the Resume)

You're passionate about the AI industry. Not just as a recruiter operating in it, but as someone who follows it, thinks about it, and is excited to be part of building it. You see AI as a tool that makes you a better recruiter, not a threat to the craft. You're curious, experimental, and energized by figuring out how to do things better and faster. You bring that same energy to finding and closing exceptional GTM talent.

The Role: 

As a Training Infrastructure Engineer, you'll design, build, and optimize the infrastructure that powers our large-scale model training operations. Your work will be essential to developing high-performance AI training infrastructure. You'll collaborate with AI researchers and engineers to create robust training pipelines, optimize distributed training workloads, and ensure reliable model development.

Key Responsibilities:

• Design and implement scalable infrastructure for large-scale model training workloads
• Develop and maintain distributed training pipelines for LLMs and multimodal models
• Optimize training performance across multiple GPUs, nodes, and data centers
• Implement monitoring, logging, and debugging tools for training operations
• Architect and maintain data storage solutions for large-scale training datasets
• Automate infrastructure provisioning, scaling, and orchestration for model training
• Collaborate with researchers to implement and optimize training methodologies
• Analyze and improve efficiency, scalability, and cost-effectiveness of training systems
• Troubleshoot complex performance issues in distributed training environments

Minimum Qualifications:

• Bachelor's degree in Computer Science, Computer Engineering, or related field, or equivalent practical experience
• 3+ years of experience with distributed systems and ML infrastructure
• Experience with PyTorch
• Proficiency in cloud platforms (AWS, GCP, Azure)
• Experience with containerization, orchestration (Kubernetes, Docker)
• Knowledge of distributed training techniques (data parallelism, model parallelism, FSDP)

Preferred Qualifications:

• Master's or PhD in Computer Science or related field
• Experience training large language models or multimodal AI systems
• Experience with ML workflow orchestration tools
• Background in optimizing high-performance distributed computing systems
• Familiarity with ML DevOps practices
• Contributions to open-source ML infrastructure or related projects

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.

The Role:

As a Technical Developer Advocate at Fireworks AI, you will serve as a technical ambassador for our state-of-the-art platform. In this role, you’ll focus on building strong relationships with developers, crafting compelling technical content, and sharing insights that directly influence our product evolution. This role calls for deep technical expertise, creative communication, and a genuine passion for community engagement.

Key Responsibilities:

• Community Engagement: Foster a vibrant developer community through forums, social media, webinars, meetups, hackathons, and conferences. Drive initiatives that encourage connection, collaboration, and innovation.
• Technical Evangelism: Deliver engaging presentations, live demos, and hands-on workshops that highlight Fireworks' capabilities and inspire platform adoption.
• Content Creation & Thought Leadership: Produce high-quality technical content—blogs, tutorials, docs, and videos—that simplify complex AI topics and provide actionable value.
• Advocacy & Feedback: Serve as a trusted advocate by capturing developer feedback and sharing insights with internal teams to inform product development.
• Cross-Functional Collaboration: Partner with Product and Engineering to translate developer needs into impactful roadmap decisions and innovations.
  Impact Measurement: Leverage data and metrics to assess engagement effectiveness and optimize community strategies.

Minimum qualifications:

• Bachelor’s degree in Computer Science, Engineering, or a related field—or equivalent practical experience.
• 3+ years in developer relations, developer advocacy, technical evangelism, or similar roles within developer products or SaaS.
• Proven experience in engaging and nurturing developer communities.
• Strong technical proficiency in programming languages (e.g., Python, JavaScript) and familiarity with AI/ML concepts.
• Excellent communication and presentation skills, with the ability to explain complex technical topics in an accessible manner.
• A data-driven mindset with strong analytical abilities.

Preferred qualifications:

• Experience in the AI or machine learning industry, with a deep understanding of generative AI technologies.
• Previous experience in a startup or fast-paced tech environment.
• Demonstrated success in creating compelling technical content for diverse developer audiences.
• Experience contributing to and engaging with open-source communities.
• Familiarity with tools for community engagement, content management, and performance analytics.