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.

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

Responsibilities

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
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
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
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