Research engineers to help accelerate post-training models specific to the GTM and sales domain, working directly with founders to fine-tune intelligence and push the Pareto Frontier.
Compensation
Competitive Early-Stage Equity
Responsibilities
Iterate on the full stack of datasets, training stages, and hyperparameters that determine model behavior
Build evals that actually capture what matters
Dig into why training produces results that don't make sense
Apply and advance techniques like RLHF, RLAIF, and constitutional approaches to shape how agents reason, act, and collaborate with humans in long-horizon tasks
Measure how performance scales with data and compute, and develop new methodologies when existing ones hit ceilings
Build advanced AI-generated content (AIGC) quality frameworks and agentic flows to generate content for proactive surfaces like Discover and Notifications.
Develop AI-generated content through advanced context engineering and agentic feedback loops to identify and deliver engaging stories globally.
Conduct advanced quality evaluations and leverage downstream dense recommendation signals and feedback to improve the performance of the AIGC stack.
Resolve key system-level bottlenecks in recommending and serving AIGC content to optimize efficiency for low-latency surfaces.
Navigate high ambiguity, drive technical innovation for AIGC quality, and influence the broader organizational machine learning strategy.
Design and build intelligent, autonomous agents that transform workflows and customer experiences using AI agent frameworks and integration with enterprise systems.
Locations (2)
California, United States
United States, United States
Compensation
San Francisco Bay Area: 156,400 - 265,700 USD Annual
All Other US Locations: 136,000 - 231,000 USD Annual
Responsibilities
Design & Build AI Agents: Develop and deploy agents capable of perception, reasoning, and autonomous action.
Integration: Integrate agents with existing external systems using REST APIs, microservices, and cloud platforms.
Optimization: Implement best practices for performance, scalability, and security in multi-agent environments.
Collaboration: Work with product managers and data scientists to align agent capabilities with business needs.
Innovation: Explore emerging technologies in Generative AI, LLM orchestration, and multi-agent frameworks.
Lead architecting next generation autonomous marketplace decisioning systems, guiding technical strategy and translating research into production systems serving millions of users.
Responsibilities
Architect and implement end-to-end reinforcement learning pipelines
Design latent world models and spatio-temporal marketplace state representations
Establish formal off-policy evaluation frameworks and safety guardrails
Build behavioural simulation systems using fine-tuned large language models
Partner with backend, streaming, and platform engineering teams to deploy low-latency decision agents
Requirements
PhD in Computer Science, Operations Research, Applied Mathematics, or related quantitative discipline
Master's degree with at least 12 years of equivalent experience
Lead the design and implementation of reinforcement learning and large language model systems for Grab's marketplace optimization, serving as a technical lead for a small team and reporting to the Head of Data Science.
Responsibilities
Design and implement end-to-end RL systems that combine model-based RL, offline RL, simulation, and online learning into a unified training pipeline.
Build latent world models and marketplace state representations that capture supply-demand interactions, location-based patterns, and behavioural signals from users and drivers.
Develop systems that optimize across multiple marketplace levers simultaneously—pricing, dispatching, and supply repositioning.
Create policy evaluation frameworks and establish monitoring systems that allow safe deployment of new decision-making policies in production.
Fine-tune open-source large language models on domain-specific data to build capabilities for prediction, reasoning, and simulation within marketplace applications.
Requirements
PhD in Computer Science, Operations Research, Applied Mathematics, or related field
Lead an independent, high-impact research agenda on large language models and agentic systems, owning projects from early hypothesis through model training, evaluation, and production deployment.
Locations (2)
San Francisco, California, United States
New York City, New York, United States
Responsibilities
Lead an independent, high-impact research agenda on large language models and agentic systems
Design and execute large-scale post-training experiments using supervised fine-tuning, reinforcement learning from human feedback, RLAIF, DPO, and emerging alignment techniques
Build novel evaluation benchmarks and methodologies
Develop scalable data synthesis and curation pipelines
Shape WRITER's model architecture and training roadmap
Requirements
Ph.D. in Computer Science, Machine Learning, NLP, or a related field
Seeking an experienced ML Infrastructure Software Engineer to build and improve infrastructure for machine learning workloads, supporting experimentation, training, evaluation, and production deployment.
Responsibilities
Design and build infrastructure for model training and production serving
Develop reliable pipelines and shared tools for ML workloads
Improve compute utilization, workload scheduling, and performance across distributed systems
Use profiling and monitoring to identify bottlenecks, troubleshoot failures, and guide improvements
Automate testing and measurement to help teams assess changes consistently
Own positioning and messaging for frontier labs and enterprise AI teams, including where the two share a story and where each needs its own.
Locations (2)
San Francisco, California, United States
New York, New York, United States
Compensation
For pay transparency, the base salary range for this full-time role in San Francisco or New York is $170,000-$200,000 per annum and negotiable for other states.
Responsibilities
Own positioning and messaging for frontier labs and enterprise AI teams
Work with product on the offerings and solution sets for each type of buyer
Run positioning interviews with AI customers and prospects
Lead enablement on strategic lab and enterprise AI pursuits
Build the repeatable playbook for taking Prolific into a new AI segment
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