Description
At Paycom, the Data Science team operates at the intersection of innovation and execution – partnering across both Product, Development, and IT to embed intelligence into every layer of our HCM platform. This highly technical function builds and optimizes the models, infrastructure, and machine learning capabilities that enable smarter decision-making and more intelligent user experiences.
Within this cross-functional ecosystem, the AI Engineer plays a critical individual contributor role, designing the infrastructure and model optimization strategies that power our most advanced AI initiatives. From fine-tuning large language models (LLMs) to designing inference and training orchestration layers, this role enables Paycom to move quickly and confidently in the evolving AI landscape. This is a high-impact position for a technical leader ready to implement sophisticated, performance-driven solutions with broad business impact.
RESPONSIBILITIES
- Design and architect scalable infrastructure systems to support efficient Machine Learning (ML) model inference and deployment.
- Lead the optimization of Kubernetes deployments to ensure high-performance, reliable, cost-effective model training and serving.
- Develop and maintain orchestration layers for complex AI/ML workloads and multi-model environments.
- Design and implement model training and fine-tuning pipelines for LLMs using cutting-edge techniques. (e.g., QLoRA, RLHF)
- Build and maintain automated systems for data preparation, evaluation, monitoring, and deployments of all ML models.
- Research and apply emerging methods in instruction tuning, model customization, LLM inferencing, and model serving. (e.g., Speculative decoding, Disaggregated attention)
- Create frameworks to evaluate fine-tuned model performance and efficiency vs. base models, ensuring ROI on customization efforts.
- Define and manage monitoring strategies for latency, throughput, resource usage, and model performance.
- Partner cross-functionally with Data Science, Product, Development, and IT to translate complex model needs into scalable technical solutions.
- Contribute to long-term AI/ML infrastructure strategy and mentor teammates on best practices.
Qualifications
RESPONSIBILITIES
- Design and architect scalable infrastructure systems to support efficient Machine Learning (ML) model inference and deployment.
- Lead the optimization of Kubernetes deployments to ensure high-performance, reliable, cost-effective model training and serving.
- Develop and maintain orchestration layers for complex AI/ML workloads and multi-model environments.
- Design and implement model training and fine-tuning pipelines for LLMs using cutting-edge techniques. (e.g., QLoRA, RLHF)
- Build and maintain automated systems for data preparation, evaluation, monitoring, and deployments of all ML models.
- Research and apply emerging methods in instruction tuning, model customization, LLM inferencing, and model serving. (e.g., Speculative decoding, Disaggregated attention)
- Create frameworks to evaluate fine-tuned model performance and efficiency vs. base models, ensuring ROI on customization efforts.
- Define and manage monitoring strategies for latency, throughput, resource usage, and model performance.
- Partner cross-functionally with Data Science, Product, Development, and IT to translate complex model needs into scalable technical solutions.
- Contribute to long-term AI/ML infrastructure strategy and mentor teammates on best practices.
QUALIFICATIONS
Education/Certification:
- Bachelor's degree in Computer Science, Engineering, or a related technical field
Experience Required:
- 3+ years of experience in ML, AI, or infrastructure engineering, with a focus on deployment and optimizing large-scale models.
- Hands-on experience fine-tuning, pre-training, and inferencing LLMs or other foundation models in a production environment.
- Worked with AI Accelerators (e.g., GPUs, TPUs) within a Kubernetes environment.
- Experience with ML tooling including vLLM, Unsloth, HF Transformers, and LoRA.
Experience Preferred:
- Prior professional experience building end-to-end ML pipelines for inference and model monitoring at scale.
- Advanced understanding of distributed systems concepts and performance tuning
- Experience leading complex technical projects and mentoring junior engineers
Skills and Abilities:
- Expert-level proficiency in Python, with Go, C++, or Rust experience a plus
- Deep understanding of ML infrastructure, GPU optimization, and Kubernetes deployment strategies
- Excellent communication and collaboration skills; able to expand technical concepts across varied audiences
- Proven ability to thrive in a fast-paced, cross-functional environment
- Self-starter with a passion for AI innovation and real-world impact
PHYSICAL DEMANDS
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of this job, the employee is regularly required to stand; walk; sit; use hands to finger, handle, or feel; reach with hands and arms; and talk or hear. The employee must regularly lift and/or move up to 25 pounds and occasionally lift and/or move up to 100 pounds. The employee must be physically fit with the ability to stand on ladders for extended period of times. Specific vision abilities required by this job include close vision, color vision, and ability to adjust focus
WORK ENVIRONMENTAL CONDITIONS
The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job.
No hazardous or significantly unpleasant conditions. (Such as in a typical office).
The noise level in the work environment is usually moderate.
Paycom is an equal opportunity employer and prohibits discrimination and harassment of any kind. Paycom makes employment decisions on the basis of business needs, job requirements, individual qualifications and merit. Paycom wants to have the best available people in every job. Therefore, Paycom does not permit its employees to harass, discriminate or retaliate against other employees or applicants because of race, color, religion, sex, sexual orientation, gender identity, pregnancy, national origin, military and veteran status, age, physical or mental disability, genetic characteristic, reproductive health decisions, family or parental status or any other consideration made unlawful by applicable laws. Equal employment opportunity will be extended to all persons in all aspects of the employer-employee relationship. This policy applies to all terms and conditions of employment, including, but not limited to, hiring, training, promotion, discipline, compensation benefits, and separation of employment. The Human Resources Department has overall responsibility for this policy and maintains reporting and monitoring procedures. Any questions or concerns should be referred to the Human Resources Department. ****To learn more about Paycom's affirmative action policy, equal employment opportunity, or to request an accommodation - Click on the link to find more information: paycom.com/careers/eeoc
Apply on company website
Find Connections via Linkedin
