Own core components of instant shopping personal intelligence—context engineering, grounding, alignment, and serving.
Design context engineering strategies and real-time retrieval.
Build SFT pipelines and implement reinforcement learning from human feedback (RLHF) and verifiable rewards (RLVR).
Use relevant APIs for data augmentation and ship low-latency, reliable inference.
Collaborate with Engineering and Data Science to run end-to-end experiments and deliver production models that improve business KPIs.
Requirements
MSc or PhD in Computer Science, Machine Learning, or equivalent research experience with significant contributions to AI/ML literature
Established experience in large-scale machine learning research with demonstrated impact on real-world systems
Deep expertise in transformer architectures, large language models, and modern pre- and post-training paradigms
Mastery of advanced fine-tuning techniques including LoRA/QLoRA, adapter methods, and parameter-efficient transfer learning
Research experience with agentic AI frameworks, multi-agent systems, and declarative programming approaches (DSPy, LangChain ecosystem)
Strong systems engineering capabilities with PyTorch, distributed training, and cloud-native ML infrastructure
Track record of publications in top-tier venues (NeurIPS, ICML, ICLR, AAAI) or equivalent industry impact
Commitment to responsible AI development and alignment research.
Benefits
Medical/Dental/Vision Insurance
401(k) Retirement Savings Plan
HSA or FSA eligibility
Long and Short-Term Disability Insurance
Mental Health Benefits
Fitness Reimbursement Program
25% employee discount & FAM Membership
Flexible PTO
Group Life Insurance
EAP through AllOne Health (formerly Carebridge)
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
machine learningreinforcement learningtransformer architectureslarge language modelsfine-tuning techniquesLoRAQLoRAPyTorchcloud-native ML infrastructuredeclarative programming
Soft skills
collaborationresearch experiencesystems engineeringcommitment to responsible AI