CommerceIQ
About this role
CommerceIQ builds AI that runs ecommerce operations for large consumer brands, with customers including Coca Cola, Nestle, Colgate Palmolive, Mondelez, Samsung and Kellogg's, and more than 900 retailers connected. This is applied machine learning rather than analytics: the posting is specific about fine tuning large models, and names parameter efficient techniques such as LoRA, prefix tuning, adapters and quantization aware training. The stack is PyTorch and TensorFlow with the Hugging Face ecosystem, and distributed training on DeepSpeed, PyTorch Lightning or Ray is welcome. It asks 3+ years alongside a master's or PhD, or equivalent experience. CommerceIQ describes itself as pre IPO and says it ships AI agents into Fortune 100 workflows weekly rather than running demos.
Who this is for
What the posting asks for:
- 3+ years of hands on experience in applied machine learning and data science, with a master's or PhD in computer science, machine learning, data science, statistics or a related field, or appropriate experience in place of the degree.
- A strong background in machine learning, deep learning and NLP, with proven experience training and fine tuning large scale models such as LLMs, transformers and diffusion models.
- Hands on expertise with parameter efficient fine tuning: LoRA, prefix tuning, adapters and quantization aware training.
- Proficiency in PyTorch, TensorFlow and the Hugging Face ecosystem.
- A basic understanding of MLOps: experiment tracking, model versioning, CI and CD for ML pipelines, and production deployment.
- Experience with large datasets, feature engineering and data pipelines using Spark, Databricks or cloud native ML services such as AWS SageMaker, GCP Vertex AI or Azure ML.
- Knowledge of GPU and TPU optimisation, mixed precision training and scaling ML workloads.
- Excellent communication, including explaining complex ML concepts to non technical audiences.
Marked mandatory in the posting:
- Demonstrated success adapting foundation models to domain specific applications through fine tuning or transfer learning.
- A strong ability to design, evaluate and improve models using robust validation strategies, bias and fairness checks, and performance optimisation.
Counts as a bonus:
- Distributed training frameworks such as DeepSpeed, PyTorch Lightning or Ray.
- Proven ability to mentor junior applied scientists and ML engineers.
Experience: the posting states 3+ years. Note the degree expectation is unusually explicit here, and the two mandatory skills are about fine tuning and evaluation rather than years served.
Location: Bengaluru. CommerceIQ is headquartered in Mountain View with teams across the US, India, Canada and the UK.
Who should apply: someone three or more years into applied ML who has actually fine tuned models rather than only called APIs. If your experience is dashboards and reporting, the mandatory requirements will screen you out, and MongoDB's data analyst role in today's list is a better fit.
- 3+ years of hands on experience in applied machine learning and data science, with a master's or PhD in computer science, machine learning, data science, statistics or a related field, or appropriate experience in place of the degree.
- A strong background in machine learning, deep learning and NLP, with proven experience training and fine tuning large scale models such as LLMs, transformers and diffusion models.
- Hands on expertise with parameter efficient fine tuning: LoRA, prefix tuning, adapters and quantization aware training.
- Proficiency in PyTorch, TensorFlow and the Hugging Face ecosystem.
- A basic understanding of MLOps: experiment tracking, model versioning, CI and CD for ML pipelines, and production deployment.
- Experience with large datasets, feature engineering and data pipelines using Spark, Databricks or cloud native ML services such as AWS SageMaker, GCP Vertex AI or Azure ML.
- Knowledge of GPU and TPU optimisation, mixed precision training and scaling ML workloads.
- Excellent communication, including explaining complex ML concepts to non technical audiences.
Marked mandatory in the posting:
- Demonstrated success adapting foundation models to domain specific applications through fine tuning or transfer learning.
- A strong ability to design, evaluate and improve models using robust validation strategies, bias and fairness checks, and performance optimisation.
Counts as a bonus:
- Distributed training frameworks such as DeepSpeed, PyTorch Lightning or Ray.
- Proven ability to mentor junior applied scientists and ML engineers.
Experience: the posting states 3+ years. Note the degree expectation is unusually explicit here, and the two mandatory skills are about fine tuning and evaluation rather than years served.
Location: Bengaluru. CommerceIQ is headquartered in Mountain View with teams across the US, India, Canada and the UK.
Who should apply: someone three or more years into applied ML who has actually fine tuned models rather than only called APIs. If your experience is dashboards and reporting, the mandatory requirements will screen you out, and MongoDB's data analyst role in today's list is a better fit.
Apply on company site
Opens job-boards.greenhouse.io, the employer's own application page. Applying is always free.