Responsibilities
- Contribute to the design and implementation of specialised Large Language Models, working with model weights, attention mechanisms, and efficient sparse architectures. You'll help translate research papers into working implementations under the guidance of senior team members.
- Participate in the lifecycle from research spike through to deployable artefact — reading papers, prototyping in notebooks, building reusable Python libraries, and deploying on GPU-accelerated cloud infrastructure. You'll work with tools like PyTorch, HuggingFace Transformers, and AWS SageMaker regularly.
- Help build and maintain evaluation frameworks to compare model variants across intelligence, reasoning capability, and inference cost. You'll run experiments, contribute to comparison tooling, and help present findings to stakeholders.
- Support and extend the cloud infrastructure (AWS CloudFormation, SageMaker, S3, GPU instances) that powers our experimentation and model serving. You'll contribute to optimising for both research velocity and cost efficiency.
- Engage with the open-source ML ecosystem, leveraging established toolkits for model development and evaluation.
- Actively participate in design discussions, contribute ideas to the team's research agenda, and provide guidance to more junior team members where appropriate
- Degree in Computer Science, Machine Learning, Mathematics, or a related quantitative field (or equivalent hands-on experience). 3+ years in ML engineering, with at least 1 year working directly with LLMs or deep learning model architectures. A relevant Master's degree would reduce the experience requirement by 1–2 years.
- Good understanding of transformer architectures, attention mechanisms, and model internals. You should be able to reason about parameter counts, tensor shapes, and how architectural choices affect model behaviour — and be eager to deepen this knowledge further.
- Solid proficiency in Python with hands-on experience in PyTorch. Familiarity with HuggingFace Transformers, tokenizers, model loading/saving, and awareness of the broader open-weight model ecosystem (Llama, Mistral, DeepSeek, and similar model families).
- Familiarity with AWS services for ML workloads — SageMaker (Notebooks/Studio), EC2 GPU instances, and S3 for model storage. Exposure to CloudFormation or other IaC tooling is a plus.
- Clean code practices, Git workflows, CI/CD (Jenkins or similar), unit testing, and the ability to write well-structured Python code — not just notebooks
- Exposure to sparse architectures, expert routing, conditional computation, or techniques for adapting and combining pre-trained models to create new, specialised variants.
- Awareness of quantization (GPTQ, AWQ, bitsandbytes), model compression, or distributed inference strategies for running large models efficiently.
- Experience with CloudFormation, Terraform, or similar tools for reproducible ML infrastructure deployments.
- Familiarity with the contact centre industry, conversational AI, or agentic AI systems is a plus but not essential — we'll teach you the domain.
- An awkward sense of humour and a genuine love for digging into model internals. We value curiosity, intellectual honesty, and people who aren't afraid to say "I don't know, but I'll figure it out."
Benefits
- AI at enterprise scale – Build, support and operate AI-powered technology used by more than 8,000 organizations worldwide. 150+ new AI features were released in the last fiscal year.
- A flexible-first culture – Join a global team of nearly 7,000 employees with flexible ways of working designed to help people do their best work.
- Growth in the AI era – Build future-ready skills through mentorship, learning programs, leadership development and education support.
- Time to recharge and give back – Benefits include paid volunteer time, August Free Fridays, well-being resources and regionally tailored programs for employees and their families.
- Recognized globally – Genesys is Great Place to Work® certified in 17 countries and 94% of employees are proud to tell others they work at Genesys.
- Our Talent Acquisition team reviews your application with the hiring team.
- A Talent Acquisition Partner will review your application and, if your background is aligned, schedule a Zoom interview.
- Next, you'll meet the hiring manager and other members of the interview team.
- We aim to keep the process focused and respectful of your time, with no more than five interviews in most cases.
- After interviews are complete, our team will follow up with the final steps.
About the company
Genesys® empowers more than 8,000 organizations worldwide to create the best customer and employee experiences. With agentic AI at its core, Genesys Cloud™ is the AI-Powered Experience Orchestration platform that connects people, systems, data and AI across the enterprise. As a result, organizations can drive customer loyalty, growth and retention while increasing operational efficiency and teamwork across human and AI workforces. To learn more, visit www.genesys.com.
Reasonable Accommodations:
If you require a reasonable accommodation to complete any part of the application process, or are limited in your ability to access or use this online application and need an alternative method for applying, you or someone you know may contact us at [email protected].
You can expect a response within 24–48 hours. To help us provide the best support, click the email link above to open a pre-filled message and complete the requested information before sending. If you have any questions, please include them in your email.
This email is intended to support job seekers requesting accommodations. Messages unrelated to accommodation—such as application follow-ups or resume submissions—may not receive a response.
Genesys is an equal opportunity employer committed to fairness in the workplace. We evaluate qualified applicants without regard to race, color, age, religion, sex, sexual orientation, gender identity or expression, marital status, domestic partner status, national origin, genetics, disability, military and veteran status, and other protected characteristics.
Please note that recruiters will never ask for sensitive personal or financial information during the application phase.