AI Innovation Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an AI Innovation Engineer based in the United States.

This is a high-impact opportunity to bridge advanced AI research with practical, production-ready engineering.
You’ll design, develop, and ship sophisticated machine learning systems that address complex business challenges.
The role spans the full AI lifecycle, from problem definition and experimentation through deployment, observability, and continuous improvement.
You’ll work with modern deep learning techniques, large-scale model training, and emerging approaches across the rapidly evolving AI landscape.
Success requires both scientific rigor and strong engineering judgment, with an emphasis on building scalable, reliable, and well-instrumented systems.
You’ll have the opportunity to evaluate cutting-edge research and translate promising techniques into meaningful real-world applications.
This role is well suited to an experienced ML professional who enjoys working at the intersection of research, innovation, and production engineering.

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.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.