Machine Learning Engineer

Sciforium•San Francisco, CA

About The Position

As a Research Engineer , you’ll work across the full foundation-model stack: pretraining and scaling , post-training and Reinforcement Learning , sandbox environments for evaluation and agentic learning , and deployment + inference optimization . You’ll build and iterate quickly on research ideas, contribute production-grade infrastructure, and help deliver models that can serve real-world use cases at scale. What you’ll work on This role spans multiple tracks - candidates may focus on one or contribute across several. Examples include: Pretraining & Scaling Train large byte-native foundation models across massive, heterogeneous corpora Design stable training recipes and scaling laws for novel architectures Improve throughput, memory efficiency, and utilization on large GPU clusters Build and maintain distributed training infrastructure and fault-tolerant pipelines Post-training & RL 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 frameworks to drive iterative improvement Explore inference-time learning and compute techniques to enhance performance Sandbox Environments & Evaluation Build scalable sandbox environments for agent evaluation and learning Create realistic, high-signal automated evals for reasoning, tool use, and safety Design offline + online environments that support RL-style training at scale Instrument environments for observability, reproducibility, and iteration speed Deployment & Inference Optimization Optimize inference throughput/latency for byte-native architectures Build high-performance serving pipelines (KV caching, batching, quantization, etc.) Improve end-to-end model efficiency, cost, and reliability in production Profile and optimize GPU kernels, runtime bottlenecks, and memory behavior

Requirements

Strong general software engineering skills (writing robust, performant systems)
Experience with training or serving large neural networks (LLMs or similar)
Solid grasp of deep learning fundamentals and modern literature
Comfort working in high-performance environments (GPU, distributed systems, etc.)

Nice To Haves

Pretraining / large-scale distributed training (FSDP/ZeRO/Megatron-style systems)
Post-training pipelines (SFT, RLHF/RLAIF, preference optimization, eval loops)
Building RL environments, simulators, or agent frameworks
Inference optimization, model compression, quantization, kernel-level profiling
Building large ETL pipelines for internet-scale data ingestion and cleaning
Owning end-to-end production ML systems with monitoring and reliability
Ability to propose and evaluate research ideas quickly
Strong experimental hygiene: ablations, metrics, reproducibility, analysis
Bias toward building — you can turn ideas into working code and results