Forecareer

Overview

We are hiring a Machine Learning Engineer / Research Engineer for a well-funded AI infrastructure company building next-generation foundation models and high-performance model serving systems.

This role sits at the intersection of research and production, spanning large-scale pretraining, post-training (RL), evaluation environments, and deployment/inference optimization. The team works hands-on across the full model lifecycle and operates at serious scale.

About the Role

As a Research-Oriented ML Engineer, you’ll work across the full foundation-model stack:

• Large-scale pretraining and scaling
• Post-training and reinforcement learning
• Sandbox environments for evaluation and agent learning
• Deployment and inference optimization for production systems

You’ll move quickly from ideas to working systems, contribute production-grade infrastructure, and help deliver models that power real-world applications at scale.

What You’ll Work On

This role spans multiple tracks. Candidates may focus on one area or contribute across several.

Pretraining & Scaling

• Train large foundation models across massive, heterogeneous datasets
• Design stable training recipes and scaling strategies for new architectures
• Improve throughput, memory efficiency, and utilization on large GPU clusters
• Build and maintain distributed, fault-tolerant training pipelines

Post-Training & Reinforcement Learning

• Develop post-training pipelines (SFT, preference optimization, RLHF / RLAIF, RL)
• Curate and generate targeted datasets to improve specific model capabilities
• Build reward models and evaluation loops for iterative improvement
• Explore inference-time learning and compute-aware techniques

Sandbox Environments & Evaluation

• Build scalable sandbox environments for agent learning and evaluation
• Design automated evaluations for reasoning, tool use, and safety
• Create offline and online environments that support RL-style training
• Instrument systems for observability, reproducibility, and fast iteration

Deployment & Inference Optimization

• Optimize inference latency and throughput for large models
• Build high-performance serving pipelines (batching, KV caching, quantization)
• Improve end-to-end efficiency, cost, and reliability in production
• Profile and optimize runtime bottlenecks, GPU kernels, and memory behavior

Ideal Candidate ProfileTechnical Strength

• Strong software engineering fundamentals (robust, performant systems)
• Experience training or serving large neural networks (LLMs or similar)
• Solid understanding of modern deep learning methods and literature
• Comfort working in high-performance, GPU-based, distributed environments

Relevant Experience (one or more)

• Large-scale distributed training (FSDP, ZeRO, Megatron-style systems)
• Post-training pipelines (SFT, RLHF / RLAIF, eval loops)
• Building RL environments, simulators, or agent frameworks
• Inference optimization, model compression, quantization, profiling
• Large-scale data pipelines for internet-scale ingestion and cleaning
• Owning production ML systems end-to-end (monitoring, reliability)

Research Orientation

• Ability to propose, test, and iterate on research ideas quickly
• Strong experimental discipline: metrics, ablations, reproducibility
• Builder mindset — turning ideas into working code and measurable results

Education

• MS or PhD in Computer Science, Machine Learning, AI, Mathematics, or related field

Benefits

• Competitive salary and meaningful equity
• Medical, dental, and vision coverage
• 401(k)
• Flexible time off
• Daily meals and snacks (on-site)

Equal Opportunity

This employer is committed to building a diverse and inclusive team and is an equal opportunity employer.

Job Type: Full-time

Pay: $200,000.00 - $275,000.00 per year

Benefits:

• 401(k)
• Dental insurance
• Flexible schedule
• Health insurance
• Paid time off
• Vision insurance

Work Location: In person