Machine Learning Engineer

As an AI/ML Research Engineer – LLM Post-Training & Evaluation , you will design and build the pipelines and tooling that connect data, evaluation, and model post-training. You’ll work on areas such as SFT, DPO/preference optimization, RLHF/RLAIF workflows, automated evaluation, experiment tracking, and multimodal model assessment .

This is a hands-on engineering role suited for someone who can bridge research and production-quality ML engineering .

Key Responsibilities

• Lead or co-lead technically complex ML engineering projects from discussion through delivery
• Build and optimize LLM training and post-training pipelines
• Develop data ingestion, preprocessing, fine-tuning, evaluation, and experiment-tracking workflows
• Build automated evaluation pipelines, benchmarks, and task-specific test harnesses
• Integrate human and AI-augmented evaluation signals into model development
• Improve reproducibility, metrics logging, regression monitoring, and experiment reliability
• Diagnose model behavior, training issues, data problems, and evaluation inconsistencies
• Work with Language Data Scientists and Applied Research Scientists to implement evaluation frameworks
• Collaborate directly with customer technical stakeholders
• Contribute to internal R&D, benchmark frameworks, evaluation tooling, and reusable ML infrastructure
• Mentor junior engineers and contribute to technical design reviews and engineering standards

Must-Have Qualifications

• 2–3+ years of relevant ML engineering, applied ML systems, or research engineering experience
• BS/MS/PhD in Computer Science, Machine Learning, AI, Applied Mathematics, or a related quantitative technical field
• Hands-on experience with LLM training, fine-tuning, or post-training
• Experience with at least one of:
• Supervised Fine-Tuning (SFT)
• Preference Optimization such as DPO
• RLHF/RLAIF-style workflows
• Foundation model/domain adaptation
• Strong Python programming and software engineering fundamentals
• Experience with modern ML frameworks such as PyTorch, JAX, or TensorFlow
• Experience with model libraries/tooling such as the Hugging Face ecosystem, vLLM, or distributed training stacks
• Experience designing LLM/ML evaluation pipelines , including metrics, datasets, and experiment comparisons
• Understanding of ML systems engineering, reproducibility, observability, and debugging
• Experience with distributed ML systems and performance optimization, preferably in GPU/accelerator environments
• Experience with large-scale data processing and workflow orchestration supporting ML workloads
• Ability to collaborate with research scientists, ML engineers, data engineers, and technical stakeholders

Good-to-Have

• Multimodal model training/evaluation involving text, image, audio, or video
• Long-context evaluation or model adaptation
• Agentic or multi-turn evaluation, tool-use simulation, or interactive environment testing
• Customer-facing technical consulting, solutions engineering, or applied research delivery
• LLM safety, alignment, robustness, or red-teaming evaluation experience
• Open-source ML/LLM contributions or relevant technical publications

Why Join Us

• Work on advanced GenAI systems involving LLM training, post-training, and evaluation
• Bridge research and engineering by turning evaluation findings into measurable model improvements
• Collaborate with AI experts across research, data science, engineering, and technical delivery
• Contribute to R&D including benchmarks, evaluation frameworks, and reusable ML infrastructure