AI Engineer III at Gopuff

United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, E-commerceIndustries

Requirements

Expertise in context engineering, RLHF/RLVR, and low-latency serving for agentic AI systems
Experience leading technical architecture, standards, and evaluation strategies connecting research to production
Proficiency in multi-modal context integration (temporal, spatial, behavioral) and real-time grounding with dynamic constraints
Knowledge of retrieval systems, memory policies, context schemas, and data contracts
Familiarity with declarative prompt/program compilation tools (e.g., DSPy)
Experience designing multi-agent orchestration patterns (e.g., LangChain/LangGraph, CrewAI, AutoGen, LlamaIndex)
Skills in supervised fine-tuning, data curation, synthetic data generation, QA, golden sets, and rubric/pairwise evaluations
Expertise in reasoning architectures including planning, tool selection, reflection, and uncertainty-aware decision-making
Knowledge of parameter-efficient adaptation strategies (LoRA/QLoRA, text-to-LoRA)
Proficiency in RLHF/RLVR pipelines, preference data loops, scalable oversight, and guardrails
Experience with policy optimization methods (DPO/PPO/GRPO/GSPO) and advanced techniques (constrained optimization, regularized objectives, KL-control)
Understanding of offline-to-online correlation via counterfactual/IPS/DR estimators and stress testing
Skills in interpretability, controllability, alignment verification, safeguards against reward hacking, and distributional robustness
Knowledge of privacy-preserving methods (data minimization, federated/on-device learning)
Expertise in low-latency inference architectures (quantization, caching, batching, streaming) with resilient fallbacks and red-teaming
Experience building evaluation frameworks coupling offline metrics with online performance and safety
Ability to use APIs for high-fidelity data augmentation to improve grounding and suggestions
Capability to design experiments, define success criteria, and iterate with Engineering and Data Science teams
Strong skills in translating product goals into technical milestones, documentation, incident response, and learning playbooks
Mentoring experience and ability to raise standards in design quality, reproducibility, and ethical rigor

Responsibilities

Lead technical efforts across context engineering, RLHF/RLVR, and low-latency serving
Define architecture, standards, and evaluation strategy connecting research to real-world impact
Mentor colleagues and influence cross-functional roadmaps
Ship production systems delivering measurable customer and business outcomes
Set strategy for context engineering to maximize precision/recall of order metrics across sessions, households, locales, and time
Architect multi-modal context integration and real-time grounding with dynamic constraint satisfaction
Establish retrieval freshness, geo/time-aware constraints, memory policies, context schemas, and data contracts
Champion declarative prompt/program compilation for systematic LLM behavior
Design multi-agent orchestration patterns for robust emergent reasoning
Lead supervised reasoning-centered fine-tuning with data curation, synthetic data, QA, golden sets, and evals
Own reasoning architecture and evaluation strategy for robust, low-latency outcomes
Drive parameter-efficient adaptation strategies with criteria for specialization vs. generalization
Architect RLHF/RLVR pipelines, preference data loops, scalable oversight, and guardrails
Own policy optimization strategy with safety considerations
Ensure robust offline-to-online correlation via estimators and stress tests
Establish interpretability, controllability, and alignment verification practices
Develop safeguards against reward hacking, unsafe exploration, and ensure content policy compliance
Advance privacy-preserving methods with privacy-by-design
Architect low-latency, cost-efficient inference with resilient fallbacks and red-teaming
Build eval frameworks coupling offline/online metrics with promotion gates
Use APIs for data augmentation strengthening grounding and suggestions
Partner with Engineering and Data Science on experiments, success criteria, and iteration
Translate product goals into technical milestones with documentation and playbooks
Mentor colleagues and elevate design quality, reproducibility, and ethical rigor

Skills

Key technologies and capabilities for this role

DSPyLangChainLangGraphCrewAIAutoGenLlamaIndexRLHFRLVRfine-tuningcontext engineeringmulti-agent orchestrationsynthetic data generation

Questions & Answers

Common questions about this position

What is the employment type for this AI Engineer III position?

This is a full-time position.

Is this role remote or does it require office work?

This information is not specified in the job description.

What key skills and expertise are required for the AI Engineer III role?

The role requires expertise in context engineering, RLHF/RLVR pipelines, multi-agent orchestration (e.g., LangChain/LangGraph, CrewAI), low-latency serving, supervised fine-tuning, DPO/PPO policy optimization, and safety practices for agentic AI systems.

What is the company culture like for this position at Gopuff?

This information is not specified in the job description.

What makes a strong candidate for the AI Engineer III role?

Strong candidates will have experience leading technical architecture in context engineering, alignment systems like RLHF/RLVR, multi-agent systems, and safety practices, with the ability to mentor colleagues and ship production systems that deliver measurable business outcomes.

Gopuff

Digital delivery service for convenience items

About Gopuff

GoPuff is a digital delivery service that specializes in providing convenience items, snacks, and beverages directly to consumers. Customers can easily place orders through the GoPuff website or app, and their selected products are delivered straight to their doorsteps. The service offers a wide range of items, from candy bars to alcoholic beverages, catering to those who want quick access to everyday goods. Revenue is generated through product sales and delivery fees, with promotional deals available to attract more customers. What sets GoPuff apart from its competitors is its extensive product selection and commitment to fast delivery, aiming to meet the needs of consumers looking for convenience.

Philadelphia, PennsylvaniaHeadquarters

2013Year Founded

$4,817.2MTotal Funding

CONVERTIBLECompany Stage

Consumer GoodsIndustries

1,001-5,000Employees

Benefits

Comprehensive medical, dental, and vision insurance

Optional FSAs and HSA plans

401(k)

Commuter benefits

Supplemental employee, spouse and child life insurance to all eligible employees

Gopuff employee discount

Career growth opportunities

Internal rewards programs

Annual performance appraisal and bonus

Risks

Increased competition from DoorDash could impact Gopuff's market share in gift card delivery.

'Santa Hours' may lead to higher operational costs without guaranteed returns.

Aldi price match could strain profit margins if not managed carefully.

Differentiation

Gopuff offers 24/7 delivery with 'Santa Hours' in London, enhancing convenience.

Partnership with Blackhawk Network enables instant gift card delivery, expanding service offerings.

Acceptance of SNAP EBT nationwide positions Gopuff as a leader in accessible grocery options.

Upsides

Collaboration with Liquid Death boosts brand visibility among younger demographics.

Aldi price match initiative attracts cost-conscious consumers, enhancing competitive edge.

'Give With Gopuff' initiative improves brand image through corporate social responsibility.

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