AI Research Scientist | Machine Learning | Deep Learning |Natural Language Processing | LLM | H[...]

Enigma; San Jose (CA); Hybrid; USD 180,000 - 250,000; Full time

Job summary

Enigma is seeking an AI Research Scientist focused on Machine Learning, Deep Learning, and NLP. The role requires building and evaluating models, adapting cutting-edge research for production, and collaborating with cross-functional teams.

You will advance research through experimentation, publications, and robust evaluation, while contributing to product roadmaps and system-level improvements in a hybrid San Jose setting.

Qualifications

• Ph.D. in CS/AI/ML or related field preferred.
• 3–5 years AI/ML research experience in applied/product environments.
• Publications in top AI/ML conferences are a plus.

Responsibilities

• Design, execute, and analyze machine learning experiments with strong baselines.
• Stay up to date with AI research and validate novel techniques.
• Define evaluation protocols including offline metrics and user studies.
• Specify data/annotation requirements and oversee quality control.
• Collaborate with domain experts, PMs, and engineering teams.
• Develop reusable research assets and documentation.
• Cowork with ML Engineers to optimize training/inference pipelines.
• Contribute to academic publications and represent the company.

Skills

ML fundamentals

PEFT/LoRA

RLHF/RLAIF

Python

C++/Java

PyTorch

Hugging Face

Statistical evaluation

Cross-functional collaboration

Education

Ph.D. in Computer Science, AI, ML, or related field

Master’s degree with exceptional research/industry experience

Tools

PyTorch

Hugging Face

NumPy

PEFT/LoRA

Job description

AI Research Scientist | Machine Learning | Deep Learning |Natural Language Processing | LLM | Hybrid | San Jose, CA

Responsibilities:

• Design, execute, and analyze machine learning experiments, establishing strong baselines and selecting appropriate evaluation metrics.
• Stay up to date with the latest AI research; identify, adapt, and validate novel techniques for company-specific use cases.
• Define rigorous evaluation protocols, including offline metrics, user studies, and adversarial (red team) testing to ensure statistical soundness.
• Specify data and annotation requirements; develop annotation guidelines and oversee quality control processes.
• Collaborate closely with domain experts, product managers, and engineering teams to refine problem statements and operational constraints.
• Develop reusable research assets such as datasets, modular code components, evaluation suites, and comprehensive documentation.
• Work alongside ML Engineers to optimize training and inference pipelines, ensuring seamless integration into production systems.
• Contribute to academic publications and represent the company in research communities, as needed.

Educational Qualifications:

• Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field is strongly preferred.
• Candidates with a master’s degree and exceptional research or industry experience will also be considered.

Industry Experience:

• 3–5 years of experience in AI/ML research roles, ideally in applied or product-focused environments.
• Demonstrated success in delivering research-driven solutions that have been deployed in production.
• Experience collaborating in cross-functional teams across research, engineering, and product.
• Publications in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ACL, CVPR) are a plus.

Technical Skills:

• Strong foundational knowledge in machine learning and deep learning algorithms.
• Hands-on experience with PEFT/LoRA, adapters, fine-tuning techniques, and RLHF/RLAIF (e.g., PPO, DPO, GRPO).
• Ability to read, implement, and adapt state-of-the‑art research papers to real‑world use cases.
• Proficiency in hypothesis‑driven experimentation, ablation studies, and statistically sound evaluations.
• Advanced programming skills in Python (preferred), C++, or Java.
• Experience with deep learning frameworks such as PyTorch, Hugging Face, NumPy, etc.
• Strong mathematical foundations in probability, linear algebra, and calculus.
• Domain expertise in one or more areas: natural language processing (NLP), symbolic reasoning, speech processing, etc.
• Ability to translate research insights into roadmaps, technical specifications, and product improvements.

AI Research Scientist | Machine Learning | Deep Learning |Natural Language Processing | LLM | Hybrid | San Jose, CA