AI Innovation Engineer

Accountabilities

• Applied AI development: Design, develop, and ship advanced machine learning and deep learning systems that solve high-impact business and technical problems.
• End-to-end ML lifecycle: Take AI initiatives from problem framing and research exploration through experimentation, model development, production deployment, monitoring, and continuous improvement.
• Model development: Train, fine-tune, evaluate, and optimize deep learning models at meaningful scale using modern machine learning methodologies and frameworks.
• Research translation: Monitor current AI/ML research, critically evaluate emerging techniques, and adapt promising approaches for practical production applications.
• Production engineering: Convert research prototypes into robust, scalable, maintainable systems with appropriate observability, safeguards, and operational reliability.
• Large-scale computing: Apply distributed training, mixed-precision techniques, and accelerator hardware to efficiently develop and operate machine learning workloads.
• Model evaluation: Establish rigorous evaluation approaches to assess model performance, reliability, and suitability for real-world use cases.
• AI innovation: Explore and contribute to emerging approaches such as large language models, retrieval-augmented generation, agentic systems, and multimodal architectures where appropriate.
• Technical communication: Clearly explain complex AI concepts, methodologies, tradeoffs, and results to technical and non-technical stakeholders.
• Cross-functional impact: Partner with engineering, research, and business stakeholders to identify opportunities where applied AI can create measurable value.
• Continuous improvement: Iterate on deployed systems using performance data, research developments, and operational feedback to improve effectiveness and reliability.

Requirements

• Education: Master’s or PhD in Computer Science, Machine Learning, Statistics, or a closely related discipline, or equivalent applied professional experience.
• Professional experience: At least 6 years of combined research and applied machine learning engineering experience.
• Programming: Strong proficiency in Python and experience with modern machine learning frameworks such as PyTorch or JAX.
• Deep learning: Hands-on experience training, fine-tuning, and evaluating deep learning models at non-trivial scale.
• Technical foundations: Strong grounding in mathematics, statistics, and the theoretical principles underlying modern machine learning.
• Production ML: Demonstrated ability to move machine learning models from research prototypes into production environments with appropriate observability, reliability, and safeguards.
• Infrastructure: Familiarity with distributed training, mixed-precision training, accelerator hardware, and large-scale ML workloads.
• Research skills: Ability to read, assess, reproduce, and adapt techniques from current AI and machine learning research literature.
• Delivery track record: Demonstrated history of successfully shipping impactful applied AI or machine learning projects.
• Communication: Strong written and verbal communication skills, including the ability to explain sophisticated technical concepts clearly.
• LLM expertise: Experience with large language model training, fine-tuning, or evaluation is preferred.
• Advanced AI: Familiarity with retrieval-augmented generation, agentic systems, or multimodal architectures is advantageous.
• Responsible AI: Exposure to model evaluation, responsible AI, alignment, and related practices is a plus.
• Research contributions: Published research at recognized AI/ML venues and contributions to open-source machine learning projects are preferred.
• Work authorization: U.S. Citizens, Green Card holders, EAD holders, and candidates eligible for H-1B transfer are encouraged to apply. New H-1B visa sponsorship is not available for this position.
• Work location: Must be based in the United States and able to work fully remotely.

Benefits

• Salary: $135,000–$210,000 annually, depending on qualifications and experience.
• Work arrangement: 100% remote position within the United States.
• Employment type: Full-time, direct W-2 employment.
• Career growth: Opportunity to work on advanced AI initiatives spanning research, experimentation, engineering, and production deployment.
• Technical exposure: Hands-on experience with modern machine learning frameworks, deep learning, distributed training, emerging AI architectures, and large-scale model development.
• Innovation environment: Opportunity to evaluate and apply cutting-edge research to practical, high-impact business challenges.
• Professional development: Exposure to evolving AI technologies and opportunities to deepen expertise across applied research and production engineering.