Senior ML Research Engineer

Your Impact & Responsibilities

As a Senior ML Research Engineer, you will be responsible for the end-to-end lifecycle of large language models: from data definition and curation, through training and evaluation, to providing robust models that can be consumed by product and platform teams.

• Own training and fine-tuning of LLMs / seq2seq models: Design and execute training pipelines for transformer-based models (encoder-decoder, decoder-only, retrievalaugmented, etc.), and fine-tune open-source LLMs on Check Point–specific data (security content, logs, incidents, customer interactions).

• Apply advanced LLM training techniques such as instruction tuning, preference / contrastive learning, LoRA / PEFT, continual pre-training, and domain adaptation where appropriate.

• Work deeply with data: define data strategies with product, research and domain experts; build and maintain data pipelines for collecting, cleaning, de-duplicating and labeling large-scale text, code and semi-structured data; and design synthetic data generation and augmentation pipelines.

• Build robust evaluation and experimentation frameworks: define offline metrics for LLM quality (task-specific accuracy, calibration, hallucination rate, safety, latency and cost); implement automated evaluation suites (benchmarks, regression tests, redteaming scenarios); and track model performance over time.

• Scale training and inference: use distributed training frameworks (e.g. DeepSpeed, FSDP, tensor/pipeline parallelism) to efficiently train models on multi-GPU / multi-node clusters, and optimize inference performance and cost with techniques such as quantization, distillation and caching.

• Collaborate closely with security researchers and data engineers to turn domain knowledge and threat intelligence into high-value training and evaluation data, and to expose your models through well-defined interfaces to downstream product and platform teams.

What You Bring

• 5+ years of hands-on work in machine learning / deep learning, including 3+ years focused on NLP / language models.

• Proven track record of training and fine-tuning transformer-based models (BERT-style, encoder-decoder, or LLMs), not just consuming hosted APIs.

• Strong programming skills in Python and at least one major deep learning framework (PyTorch preferred; TensorFlow).

• Solid understanding of transformer architectures, attention mechanisms, tokenization, positional encodings, and modern training techniques.

• Experience building data pipelines and tools for large-scale text / log / code processing (e.g. Spark, Beam, Dask, or equivalent frameworks).

• Practical experience with ML infrastructure, such as experiment tracking (Weights & Biases, MLflow or similar), job orchestration (Airflow, Argo, Kubeflow, SageMaker, etc.), and distributed training on multi-GPU systems.

• Strong software engineering practices: version control, code review, testing, CI/CD, and documentation.

• Ability to own research and engineering projects end-to-end: from idea, through prototype and controlled experiments, to models ready for integration by product and platform teams.

• Good communication skills and the ability to work closely with non-ML stakeholders (security experts, product managers, engineers).

Nice to have

• Experience with RLHF / preference optimization, safety alignment, or other humanfeedback-in-the-loop approaches to training LLMs.

• Experience with retrieval-augmented generation (RAG), dense retrieval, vector databases, and embedding training.

• Background in security / cyber domains such as threat detection, malware analysis, logs, or SOC tools.

• Experience with multilingual models (e.g., Hebrew + English) and cross-lingual training.

• Experience in a product environment where models must meet reliability, scale, and cost constraints.

Why Join Us

• Work at the intersection of cutting-edge AI and real-world cyber security, with immediate impact on global customers.

• Own large-scale ML training and evaluation in a production setting, with a focus on research and model quality rather than agent development.

• Collaborate with experienced ML engineers, researchers, and security experts in a fastmoving, supportive environment.

• Access to modern GPU infrastructure and large, unique datasets from one of the world’s leading cyber security vendors.