Lead Data Scientist-Deep Learning Specialist

Why choose us?

Are you ready to take the next step in your career? Join us for an exciting opportunity at Albertsons Companies, where innovation and customer service go hand-in-hand!

At Albertsons Companies, we are looking for someone who’s not just seeking a job, but someone who wants to make an impact. In this role, you’ll have the opportunity to lead, innovate, and contribute to the growth of a company that values great service and lasting customer relationships. This position offers the chance to work in a fast-paced, dynamic environment that’s constantly evolving.

Bring your flavor  

Building the future of food and well-being starts with you. Join our team and bring your best self to the table.

Main responsibilities:

• Design end-to-end deep learning model development, from problem framing and target definition through architecture selection, training strategy, evaluation, and iteration within a Databricks Lakehouse environment 
• Architect and build end-to-end deep learning pipelines, from data ingestion and feature engineering to training, deployment, scaling, and monitoring 
• Build rigorous evaluation frameworks using offline metrics and explainability methods such as SHAP-based feature importance and prediction-level explanations. 
• Implement distributed training and large-scale data processing using Apache Spark 
• Build scalable batch and real-time inference pipelines integrated with Databricks workflows 
• Lead fine-tuning and adaptation of large models and foundation models using custom data, with checkpoints, experiments, and model artifacts tracked in MLflow and prepared for governed deployment 
• Optimize data pipelines and model performance for scalability, latency, and cost efficiency 
• Collaborate with cross-functional teams to productionize ML solutions on the Lakehouse 

The position will be based in Pleasanton, CA. 
 

We are looking for candidates who possess the following:

Required Qualifications 

• Proven experience leading deep learning model development for complex business problems, including problem formulation, experimentation, evaluation, and productionization.  
• Strong hands-on expertise in PyTorch or TensorFlow and modern neural architectures, with experience scaling training using multi-GPU or distributed approaches  
• Deep hands-on experience with Databricks, including Delta Lake, Spark, and MLflow, Unity Catalog, governance, and security 
• Strong experience with distributed computing and large-scale data processing (Apache Spark) 
• Proficiency in Python and ML/data ecosystems (NumPy, Pandas, Scikit-learn, PySpark) 
• Strong understanding of feature engineering and data pipeline design in a Lakehouse architecture 
• Expertise in distributed training and inference (multi-GPU, multi-node systems) 
• Experience designing high-throughput, low-latency inference systems 
• Experience building feature stores and reusable ML components within Databricks 
   

Preferred Qualifications 

• Experience deploying large-scale deep learning models (e.g., LLMs, recommendation systems) on Databricks 
• Experience with cloud platforms (AWS, Azure, GCP) alongside Databricks 
• Experience with streaming pipelines (Structured Streaming, Kafka integration) 
• Experience with generative AI, LLM fine-tuning, or foundation models 
• Background in retail, e-commerce, or supply chain analytics 

We also provide a variety of benefits including:

• Competitive wages paid weekly
• Access to up to 50% of your earned wages before payday, via our partnership with Stream
• Associate discounts
• Health and financial well-being benefits for eligible associates (Medical, Dental, 401k and more!)
• Time off (vacation, holidays, sick pay).  For eligibility requirements please visit myACI Benefits
• Leaders invested in your training, career growth and development
• An inclusive work environment with talented colleagues who reflect the communities we serve

Our Values – Click below to view video:   ACI Values

A copy of the full job description can be made available to you.

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