Research Engineer, Forge

Mistral AI

Research Engineer building Forge workflows for Mistral’s full-stack AI solutions. Improving LLM post-training, evaluation, data pipelines, and scalable deployment systems.

Posted 9/4/2026full-timeParis • 🇫🇷 FranceMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates strong Python engineering skills and hands-on experience with PyTorch or JAX, focusing on building and improving ML workflows, including training, evaluation, and deployment. Capable of collaborating with cross-functional teams to enhance system performance and reliability in fast-paced environments.

Highest-signal resume keywords

Python EngineeringML Workflow DevelopmentDistributed TrainingPost-Training EvaluationClear Communication

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Hard Skills

PythonPyTorchJAXLLM TrainingDebuggingData PipelinesTestingCode ReviewOperational OwnershipInfrastructure Fundamentals

Soft Skills

Clear CommunicationHigh AgencyLow Ego

Tools & Technologies

FSDPDeepSpeedMegatronSLURMRayKubernetesKueueKarpenterSkypilot

Industry Keywords

Model AdaptationSynthetic Data GenerationLarge-Scale ML SystemsObservabilityReproducibilityMaintainable AbstractionsScalable SystemsBottleneck Identification

Tech Stack

Tools & technologies

CloudKubernetesPythonPyTorchRay

About the role

Key responsibilities & impact

Turn real customer requirements into reliable training and deployment workflows
Work end-to-end across model adaptation and post-training, evaluation, data, and infrastructure
Build and improve post-training and evaluation workflows for CPT, SFT, RL, and distillation
Develop tools and pipelines for synthetic data generation, data curation, training, evaluation, and deployment
Debug and harden large-scale ML systems, including distributed training, scheduling/execution, checkpointing, observability, and reproducibility
Improve the Forge codebase through clear APIs, tests, documentation, and maintainable abstractions
Advance the RL training stack, including high-throughput asynchronous rollout and scalable post-training systems
Ensure Forge deployment is seamless and adaptable across diverse clients, hardware, software stacks, cloud, and on-premises environments
Partner with researchers and infrastructure engineers to translate bottlenecks into concrete system improvements
Collaborate with scientists, engineers, product, and customer-facing teams to ship maintainable and trusted Forge projects

Requirements

What you’ll need

Strong Python engineering skills and experience working in large codebases, including testing, code review, CI, and operational ownership
Hands-on experience with PyTorch, JAX, or similar
Strong systems and infrastructure fundamentals
Experience with LLM training or post-training, including fine-tuning, RL, distillation, evaluation, and/or data pipelines
Excellent debugging skills in distributed jobs, data issues, quality regressions, and infrastructure failures
Clear communication with technical and non-technical stakeholders
High agency, low ego, and comfort in fast-moving, under-specified environments
Nice to have: distributed training experience with FSDP, DeepSpeed, Megatron, or similar
Nice to have: cluster/orchestration experience with SLURM, Ray, Kubernetes, Kueue, Karpenter, Skypilot, or similar
Nice to have: experience building reliable ML infrastructure, evaluation systems, or large-scale data processing pipelines
Nice to have: research experience in LLMs, agents, multimodal models, reasoning, code, or domain adaptation
Nice to have: open-source contributions, publications, or widely used internal tooling
Nice to have: experience training multi-billion-parameter models and on petabyte- and exabyte-scale datasets
Nice to have: ability to identify bottlenecks across the stack and drive improvements from first principles

Benefits

Comp & perks

Healthcare coverage
Parental leave
Retirement plans
Relocation support
Wellness programs
Meal allowances
Transportation allowances
Other location-specific perks