Applied ML Engineer

About This Opportunity

We are representing a confidential AI/ML organization seeking an Applied ML Engineer based in France. This role sits at the intersection of machine learning research, rigorous experimentation, and production engineering. You'll transform research concepts into measurable experiments and reliable systems, working hands-on with modern ML models while building production-grade infrastructure that makes complex evaluations repeatable and accessible.

Our client values technical judgment, ownership, scientific rigor, and full-stack capability. This is an opportunity to help translate emerging ML verification techniques into trustworthy, practical systems.

Responsibilities

• Reproduce and evaluate machine learning research methods using open-weight and API-accessible models
• Design comprehensive evaluation frameworks including datasets, probes, scoring approaches, baselines, calibration tests, and experiment harnesses
• Work directly with model internals: weights, logits, hidden states, activations, and inference infrastructure
• Build and extend evaluation infrastructure covering experiment runners, judges, persistence, orchestration, reporting, and reproducibility
• Develop intuitive product experiences for experiment configuration, execution, traces, comparisons, and review workflows
• Investigate how verification methods perform when models undergo fine-tuning, merging, quantization, distillation, or other modifications
• Design controlled experiments that distinguish meaningful signals from artifacts and confounders
• Produce clear technical reports separating measured evidence from interpretation
• Deliver production-quality systems with APIs, asynchronous jobs, databases, observability, testing, and deployment
• First six months: reproduce at least one published model-verification method, build a repeatable verification runner, and conduct controlled experiments across various model variants

What We're Looking For

Required qualifications:

• Strong Python engineering skills with hands-on PyTorch and Hugging Face Transformers experience
• Solid understanding of ML evaluation: dataset design, baselines, metrics, calibration, false positives/negatives, statistical uncertainty, and reproducibility
• Ability to read ML research papers critically and implement methods from first principles
• Professional software engineering experience beyond notebooks: APIs, asynchronous jobs, databases, logging, testing, deployment, and documentation
• Familiarity with open-weight models and practical understanding of modern LLM inference systems
• Sufficient React/TypeScript knowledge to help make experiments and results understandable to users
• Strong experimental and analytical judgment, particularly distinguishing what evidence demonstrates versus suggests
• High ownership and initiative with ability to identify problems, propose solutions, and drive projects independently
• Comfort in fast-moving environments where priorities evolve and engineers operate across multiple functions

Valued experience (plusses):

• Model provenance, fingerprinting, watermarking, distillation detection, red-teaming, safety evaluation, or interpretability work
• Activation and representation analysis, probing, model hooks, or other model-internals techniques
• Evaluation and inference infrastructure such as DSPy, LiteLLM, Temporal, Ray, vLLM, PostgreSQL/pgvector
• Next.js, React, TypeScript, data visualization, or experiment dashboards
• Running and serving open-weight models on GPUs with understanding of latency, throughput, memory, precision, and cost trade-offs
• Designing adversarial evaluations or testing against deliberate evasion attempts
• Strong commitment to production-quality code, tests, tooling, and documentation

How We Work With You

When you apply through Get A Job.ai, our talent team will review your background against the role requirements. We'll conduct an initial screening to understand your experience with ML evaluation, production engineering, and model internals work. Qualified candidates will be presented to our client for consideration.

Important: Please apply only through Get A Job.ai. Do not attempt to contact the client directly, as all applications are managed through our recruiting process.

Pay

Compensation details will be discussed during the screening process and are competitive for the French market. Additional benefits may include flexibility arrangements, healthcare, and other components according to the employer's package and local practices.

Equal Opportunity: Get A Job.ai is committed to fair and objective candidate evaluation. We welcome applications from all qualified individuals regardless of background, identity, or personal characteristics.