AI Innovation Engineer

6+ years

Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.

Job Summary:

We are seeking an AI Innovation Engineer to bridge cutting-edge applied research and production engineering, designing and shipping advanced machine learning systems that solve high-impact business problems. The role blends scientific rigor with practical software engineering, requiring deep understanding of modern ML and deep learning techniques alongside the ability to build robust, scalable, and well-instrumented production pipelines. The ideal candidate stays current with the rapidly evolving AI research landscape, can critically evaluate new techniques for real-world applicability, and is comfortable operating across the full lifecycle from problem framing and experimentation to deployment and continuous improvement.

Required Qualifications

• Master’s or PhD in Computer Science, Machine Learning, Statistics, or a closely related field; or equivalent applied experience.

• Six or more years of combined research and applied ML engineering experience.

• Strong proficiency in Python and modern ML frameworks such as PyTorch or JAX.

• Hands-on experience training, fine-tuning, and evaluating deep learning models at non-trivial scale.

• Solid grounding in mathematics, statistics, and the theoretical foundations of modern ML.

• Experience taking ML models from research prototype to production with appropriate observability and safeguards.

• Familiarity with distributed training, mixed-precision training, and accelerator hardware.

• Strong written and verbal communication skills, including ability to explain complex methods clearly.

• Demonstrated ability to read, evaluate, and adapt techniques from current research literature.

• Track record of shipping impactful applied AI projects.

• Published research at top-tier AI/ML venues.

• Experience with large language model training, fine-tuning, or evaluation.

• Familiarity with retrieval-augmented generation, agentic systems, or multimodal architectures.

• Exposure to responsible AI, model evaluation, and alignment practices.

• Experience contributing to open-source ML projects.