Forward Deployed ML Engineer

Want to be a part of a tight-knit team responsible for creating and publishing some of the most popular and critically-acclaimed games in the world including such videogame franchises as Grand Theft Auto, Max Payne, Red Dead and much more? At Rockstar Games, we focus intently on quality and content to proudly produce exactly the kind of games we would want to play ourselves. A career at Rockstar Games is about passion and commitment, to the projects and to each other, all the while working on some of the most creatively rewarding and challenging projects to be found in any entertainment medium with some of the most talented people in the industry. We offer successful candidates a highly competitive salary and compensation package including a comprehensive benefits package for all full-time hires. Rockstar Games is an equal opportunity employer. If you have any questions about Rockstar Games unrelated to job opportunities, please direct them to mouthoff@rockstargames.com or visit rockstargames.com/newswire

Rockstar is recruiting for a forward-deployed machine learning engineer role at a leading AI infrastructure company. The client is building the AI backbone for the next generation of intelligent products, helping fast-growing AI startups design, fine-tune, evaluate, deploy, and maintain specialized models across text, vision, and embeddings. Think of it as a full-stack backend for training, RL, inference, evaluation, and long-term model maintenance. Their customers are Series A–C AI companies building enterprise-grade products, and their promise is simple: they make AI systems better.

About the Company
The company is building the AI backbone for the next generation of intelligent products. It helps fast-growing AI startups design, fine-tune, evaluate, deploy, and maintain specialized models across text, vision, and embeddings. Think of it as a full-stack backend for training, RL, inference, evaluation, and long-term model maintenance.

Its customers are Series A–C AI companies building enterprise-grade products. Its promise is simple: it makes your AI system better.

The Role
(Remote, open globally)
The company is hiring a Forward Deployed Machine Learning Engineer (FD-MLE) to work directly with customers to deploy, adapt, and operate production ML systems on top of its platform.

This is a high-execution, high-ownership role. The engineer will be embedded in customer problems, shipping real models into real production environments—often under tight timelines and ambiguous requirements. If you enjoy being close to users, moving fast, and doing the unglamorous work required to make ML systems actually work, this role is for you.

Why This Role Matters
AI infrastructure often breaks down at the last mile—between a promising model and a reliable, scalable production system. As a Forward Deployed MLE, you are the connective tissue between the platform and customer success.

You’ll:
- Turn cutting-edge ML workflows into production-ready systems
- Unblock customers facing data, training, inference, or deployment challenges
- Feed real-world learnings back into product and platform design
This role is ideal for early-career ML engineers who want maximum learning velocity, deep exposure to real systems, and accelerated responsibility.

What You’ll Do
Customer-Facing Execution
- Deploy, fine-tune, and serve ML models in production environments (text, vision, embeddings, RL-adjacent workflows).
- Work hands-on with customer data, model architectures, training loops, and inference stacks.
- Debug performance issues across training, evaluation, latency, cost, and reliability.
- Adapt the platform to customer-specific workflows and constraints.

Systems & Infrastructure
- Build and maintain model-serving pipelines (batch and real-time).
- Optimize inference performance (throughput, latency, cost).
- Help productionize evaluation, monitoring, and retraining workflows.
- Work across cloud infrastructure, GPUs, and ML tooling stacks.

Feedback & Iteration
- Act as the “voice of the customer” to internal product and engineering teams.
- Identify recurring patterns, edge cases, and gaps in the platform.
- Contribute to internal tooling, templates, and best practices.

Who You Are
Required
- 1–3 years of production ML engineering experience
- You have deployed models that serve real users in production
- You’ve worked on training, inference, or ML systems end-to-end
- Strong fundamentals in ML engineering: data pipelines, model training, evaluation, and serving.
- Comfortable writing production-quality code and debugging complex systems.
- Extremely diligent and hardworking
- This is an execution-heavy role where effort and follow-through matter
- You’re comfortable putting in the hours when needed to get things working
- Clear communicator who can work directly with customers and internal teams.

Nice to Have
- Experience with LLMs, fine-tuning, embeddings, or RL-style workflows.
- Exposure to GPU workloads, distributed training, or high-throughput inference.
- Background in infra-heavy environments (ML platforms, data systems, dev tools).
- Interest in customer-facing or forward-deployed roles.

Work Environment
- Globally remote
- High trust, high autonomy
- Fast-moving, early-stage company with direct access to founders
- Outcomes > process

Why Join
- Work on real ML systems—not demos or research projects
- Rapid skill growth through exposure to diverse customer problems
- Ownership and responsibility early in your career
- Build infrastructure that powers the next generation of AI products

Join Rockstar as a Forward Deployed ML Engineer to work directly with customers deploying and maintaining production ML systems in a remote, global environment.

Spain

<p>Rockstar is recruiting for a forward-deployed machine learning engineer role at a leading AI infrastructure company. The client is building the AI backbone for the next generation of intelligent products, helping fast-growing AI startups design, fine-tune, evaluate, deploy, and maintain specialized models across text, vision, and embeddings. Think of it as a full-stack backend for training, RL, inference, evaluation, and long-term model maintenance. Their customers are Series A–C AI companies building enterprise-grade products, and their promise is simple: they make AI systems better.

About the Company
The company is building the AI backbone for the next generation of intelligent products. It helps fast-growing AI startups design, fine-tune, evaluate, deploy, and maintain specialized models across text, vision, and embeddings. Think of it as a full-stack backend for training, RL, inference, evaluation, and long-term model maintenance.

Its customers are Series A–C AI companies building enterprise-grade products. Its promise is simple: it makes your AI system better.

The Role
(Remote, open globally)
The company is hiring a Forward Deployed Machine Learning Engineer (FD-MLE) to work directly with customers to deploy, adapt, and operate production ML systems on top of its platform.

This is a high-execution, high-ownership role. The engineer will be embedded in customer problems, shipping real models into real production environments—often under tight timelines and ambiguous requirements. If you enjoy being close to users, moving fast, and doing the unglamorous work required to make ML systems actually work, this role is for you.

Why This Role Matters
AI infrastructure often breaks down at the last mile—between a promising model and a reliable, scalable production system. As a Forward Deployed MLE, you are the connective tissue between the platform and customer success.

You’ll:
- Turn cutting-edge ML workflows into production-ready systems
- Unblock customers facing data, training, inference, or deployment challenges
- Feed real-world learnings back into product and platform design
This role is ideal for early-career ML engineers who want maximum learning velocity, deep exposure to real systems, and accelerated responsibility.

What You’ll Do
Customer-Facing Execution
- Deploy, fine-tune, and serve ML models in production environments (text, vision, embeddings, RL-adjacent workflows).
- Work hands-on with customer data, model architectures, training loops, and inference stacks.
- Debug performance issues across training, evaluation, latency, cost, and reliability.
- Adapt the platform to customer-specific workflows and constraints.

Systems & Infrastructure
- Build and maintain model-serving pipelines (batch and real-time).
- Optimize inference performance (throughput, latency, cost).
- Help productionize evaluation, monitoring, and retraining workflows.
- Work across cloud infrastructure, GPUs, and ML tooling stacks.

Feedback & Iteration
- Act as the “voice of the customer” to internal product and engineering teams.
- Identify recurring patterns, edge cases, and gaps in the platform.
- Contribute to internal tooling, templates, and best practices.

Who You Are
Required
- 1–3 years of production ML engineering experience
- You have deployed models that serve real users in production
- You’ve worked on training, inference, or ML systems end-to-end
- Strong fundamentals in ML engineering: data pipelines, model training, evaluation, and serving.
- Comfortable writing production-quality code and debugging complex systems.
- Extremely diligent and hardworking
- This is an execution-heavy role where effort and follow-through matter
- You’re comfortable putting in the hours when needed to get things working
- Clear communicator who can work directly with customers and internal teams.

Nice to Have
- Experience with LLMs, fine-tuning, embeddings, or RL-style workflows.
- Exposure to GPU workloads, distributed training, or high-throughput inference.
- Background in infra-heavy environments (ML platforms, data systems, dev tools).
- Interest in customer-facing or forward-deployed roles.

Work Environment
- Globally remote
- High trust, high autonomy
- Fast-moving, early-stage company with direct access to founders
- Outcomes > process

Why Join
- Work on real ML systems—not demos or research projects
- Rapid skill growth through exposure to diverse customer problems
- Ownership and responsibility early in your career
- Build infrastructure that powers the next generation of AI products</p><p>Benefits:</p><ul><li> Work on real ML systems, not just demos or research projects</li><li> Rapid skill growth through exposure to diverse customer problems</li><li> Ownership and responsibility early in your career</li><li> Build infrastructure that powers next-generation AI products</li><li> High autonomy and trust in a remote environment</li><li> Fast-moving, early-stage company environment</li><li> Direct access to founders</li></ul>

Rockstar Games

Forward Deployed ML Engineer Join Rockstar as a Forward Deployed ML Engineer to work directly with customers deploying and maintaining production ML systems in a remote, global environment. Rockstar is recruiting for a forward-deployed machine learning engineer role at a leading AI infrastructure company. The client is building the AI backbone for the next generation of intelligent products, helping fast-growing AI startups design, fine-tune, evaluate, deploy, and maintain specialized models across text, vision, and embeddings. Think of it as a full-stack backend for training, RL, inference, evaluation, and long-term model maintenance. Their customers are Series A–C AI companies building enterprise-grade products, and their promise is simple: they make AI systems better.

About the Company
The company is building the AI backbone for the next generation of intelligent products. It helps fast-growing AI startups design, fine-tune, evaluate, deploy, and maintain specialized models across text, vision, and embeddings. Think of it as a full-stack backend for training, RL, inference, evaluation, and long-term model maintenance.

Its customers are Series A–C AI companies building enterprise-grade products. Its promise is simple: it makes your AI system better.

The Role
(Remote, open globally)
The company is hiring a Forward Deployed Machine Learning Engineer (FD-MLE) to work directly with customers to deploy, adapt, and operate production ML systems on top of its platform.

This is a high-execution, high-ownership role. The engineer will be embedded in customer problems, shipping real models into real production environments—often under tight timelines and ambiguous requirements. If you enjoy being close to users, moving fast, and doing the unglamorous work required to make ML systems actually work, this role is for you.

Why This Role Matters
AI infrastructure often breaks down at the last mile—between a promising model and a reliable, scalable production system. As a Forward Deployed MLE, you are the connective tissue between the platform and customer success.

You’ll:
- Turn cutting-edge ML workflows into production-ready systems
- Unblock customers facing data, training, inference, or deployment challenges
- Feed real-world learnings back into product and platform design
This role is ideal for early-career ML engineers who want maximum learning velocity, deep exposure to real systems, and accelerated responsibility.

What You’ll Do
Customer-Facing Execution
- Deploy, fine-tune, and serve ML models in production environments (text, vision, embeddings, RL-adjacent workflows).
- Work hands-on with customer data, model architectures, training loops, and inference stacks.
- Debug performance issues across training, evaluation, latency, cost, and reliability.
- Adapt the platform to customer-specific workflows and constraints.

Systems & Infrastructure
- Build and maintain model-serving pipelines (batch and real-time).
- Optimize inference performance (throughput, latency, cost).
- Help productionize evaluation, monitoring, and retraining workflows.
- Work across cloud infrastructure, GPUs, and ML tooling stacks.

Feedback & Iteration
- Act as the “voice of the customer” to internal product and engineering teams.
- Identify recurring patterns, edge cases, and gaps in the platform.
- Contribute to internal tooling, templates, and best practices.

Who You Are
Required
- 1–3 years of production ML engineering experience
- You have deployed models that serve real users in production
- You’ve worked on training, inference, or ML systems end-to-end
- Strong fundamentals in ML engineering: data pipelines, model training, evaluation, and serving.
- Comfortable writing production-quality code and debugging complex systems.
- Extremely diligent and hardworking
- This is an execution-heavy role where effort and follow-through matter
- You’re comfortable putting in the hours when needed to get things working
- Clear communicator who can work directly with customers and internal teams.

Nice to Have
- Experience with LLMs, fine-tuning, embeddings, or RL-style workflows.
- Exposure to GPU workloads, distributed training, or high-throughput inference.
- Background in infra-heavy environments (ML platforms, data systems, dev tools).
- Interest in customer-facing or forward-deployed roles.

Work Environment
- Globally remote
- High trust, high autonomy
- Fast-moving, early-stage company with direct access to founders
- Outcomes > process

Why Join
- Work on real ML systems—not demos or research projects
- Rapid skill growth through exposure to diverse customer problems
- Ownership and responsibility early in your career
- Build infrastructure that powers the next generation of AI products Forward Deployed ML Engineer Engineer Rockstar Games computer-games computer-software consumer-internet consumers entertainment film film-distribution film-production games gaming information-technology internet media motion-pictures services video-games online-audio-and-video-media media-production entertainment-providers mobile-gaming-apps software-development

Rockstar is hiring a Forward Deployed ML Engineer to collaborate with clients on deploying and optimizing machine learning models in production. The role offers high ownership, fast-paced learning, and the opportunity to work on real AI infrastructure projects globally.

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