[Remote] AI/ML Research Engineer, LLM Post-Training & Evaluation

Descrição da Vaga

Requisitos

• 2-3 years of relevant industry or research engineering experience in ML/AI systems
• Hands-on experience with LLM training / fine-tuning / post-training, including at least one of: supervised fine-tuning (SFT), preference optimization (e.g., DPO or related methods), RLHF / RLAIF-style workflows, task- or domain-adaptation of foundation models
• Strong programming skills in Python and experience building production-quality ML code
• Experience with modern ML frameworks (e.g., PyTorch, JAX, TensorFlow) and model libraries/tooling (e.g., Hugging Face ecosystem, vLLM, distributed training stacks)
• Experience designing and implementing evaluation pipelines for LLM/ML systems, including metrics computation, dataset handling, and experiment comparisons
• Strong understanding of data pipelines and ML systems engineering, including reproducibility, observability, and debugging
• Experience with large-scale data processing and workflow orchestration in support of model training/evaluation
• Ability to collaborate directly with technical stakeholders including research scientists, ML engineers, data engineers, and customer technical leads
• Strong written and verbal communication skills, including the ability to explain complex technical tradeoffs to both technical and non-technical audiences
• Experience training, fine-tuning, and evaluating transformer-based models
• Understanding of post-training workflows and model iteration loops
• Familiarity with inference-time considerations (latency, throughput, memory/performance tradeoffs) where relevant to evaluation or deployment
• Experience implementing automated evaluation pipelines and test harnesses
• Experience with experiment tracking, versioning, and reproducibility practices
• Ability to assess metric quality and ensure consistency across model comparisons
• Proficiency in Python and strong software engineering fundamentals
• Experience with data processing pipelines, storage formats, and scalable dataset workflows
• Familiarity with CI/CD, testing, and engineering quality practices for ML systems
• Experience with multimodal model training/evaluation (text + image/audio/video)
• Experience with long-context evaluation and/or model adaptation for long-context tasks
• Experience with agentic or multi-turn evaluation harnesses, tool-use simulation, or interactive environment testing
• Experience working in customer-facing technical consulting, solutions engineering, or applied research delivery
• Familiarity with LLM safety, alignment, robustness, or red-teaming evaluation approaches
• Contributions to open-source ML/LLM tooling or published technical work in relevant areas

Responsabilidades

• Lead or co-lead technically complex ML engineering projects from initial customer discussions through implementation and delivery
• Design, build, and improve LLM training and post-training pipelines, including data ingestion, preprocessing, fine-tuning, evaluation, and experiment tracking
• Implement and optimize evaluation systems for LLMs and multimodal models, including offline benchmarks and task-specific test harnesses
• Integrate human-in-the-loop and AI-augmented evaluation signals into model development workflows
• Build robust infrastructure and tooling for reproducible experimentation, metrics logging, and regression monitoring
• Diagnose model behavior and pipeline failures, including data issues, training instability, metric inconsistencies, and evaluation drift
• Collaborate with Language Data Scientists and Applied Research Scientists to translate evaluation frameworks into executable systems
• Work closely with customer technical stakeholders to understand goals, constraints, and success criteria; propose and implement technically sound solutions
• Contribute to internal research and platform development, including benchmark frameworks, evaluation tooling, and post-training workflow improvements
• Contribute to best practices and standards for LLM training, evaluation, and quality assurance across projects
• Mentor junior engineers and contribute to technical design reviews, documentation, and engineering rigor across the team