Research Engineer, Forge

About This Opportunity

Our talent team is representing a confidential AI organization seeking a Research Engineer to join their applied science division in Paris. You'll bridge research experimentation and production systems, turning customer requirements into reliable training and deployment workflows for enterprise AI solutions.

This is a hands-on engineering role working end-to-end across model adaptation, post-training techniques (CPT/SFT/RL/distillation), evaluation systems, data pipelines, and infrastructure. You'll collaborate with scientists, engineers, product teams, and customer-facing colleagues to ensure projects ship reliably and maintainably.

Responsibilities

• Build and improve post-training and evaluation workflows, transforming prototypes into repeatable production recipes
• Develop tools and pipelines for synthetic data generation, data curation, model training, evaluation, and deployment
• Debug and harden large-scale ML systems including distributed training, scheduling, checkpointing, observability, and reproducibility
• Improve codebases through clear APIs, comprehensive testing, documentation, and maintainable abstractions
• Advance RL training infrastructure for high-throughput async rollout and scalable post-training at frontier-model scale
• Ensure deployment solutions work across diverse client environments (varied hardware, software stacks, cloud and on-premises)
• Partner with researchers and infrastructure engineers to identify bottlenecks and implement system improvements

What We're Looking For

Required:

• Strong Python engineering skills with experience in large codebases (testing, code review, CI, operational ownership)
• Hands-on experience with PyTorch, JAX, or similar frameworks
• Solid systems and infrastructure fundamentals
• Experience with LLM training or post-training: fine-tuning, RL, distillation, evaluation, and/or data pipelines
• Excellent debugging skills in ambiguous distributed systems (distributed jobs, data issues, quality regressions, infrastructure failures)
• Clear communication abilities with both technical and non-technical stakeholders
• High agency, collaborative mindset, and comfort in fast-moving environments

Nice to Have:

• Distributed training experience (FSDP, DeepSpeed, Megatron, etc.)
• Cluster/orchestration experience (SLURM, Ray, Kubernetes, Kueue, Karpenter, Skypilot, etc.)
• Experience building ML infrastructure, evaluation systems, or large-scale data processing pipelines
• Research background in LLMs, agents, multimodal models, reasoning, code, or domain adaptation
• Open-source contributions, publications, or widely used internal tooling
• Experience training multi-billion-parameter models or working with petabyte-scale datasets
• Ability to identify bottlenecks across the stack and drive improvements from first principles

How We Work With You

When you apply through Get A Job.ai, one of our recruiters will screen your profile and discuss the opportunity in detail. If there's a strong match, we'll submit your candidacy to our client and guide you through their interview process. Please do not contact the client directly—all communication flows through our team to ensure the best outcome for your application.

Pay

Compensation details will be discussed during the screening process and are competitive for this level in the Paris market. Benefits typically include healthcare coverage, parental leave, retirement plans, and location-specific perks.

Equal Opportunity: Get A Job.ai is committed to inclusive hiring practices and welcomes candidates from all backgrounds.