Research Engineer, ML Systems & Multimodal Pipelines

Breakout Ventures; San Francisco (CA); On-site; USD 180,000 - 250,000; Full time

Benefits offered by this job

Health insurance

Relocation assistance

Workspace stipend

Team events

Job summary

Constellation is seeking a Research Engineer in San Francisco to bridge data and models, accelerating long-horizon multimodal training. You will build data pipelines, libraries, and evaluation tools while collaborating with engineering and research teams.

You will optimize distributed training, manage data versioning, and turn prototypes into repeatable pipelines that scale across GPUs. Strong Python/PyTorch skills are essential.

Qualifications

• 3+ years building ML systems or research infrastructure, including distributed training runs on 100+ GPUs.
• Expert-level Python and deep knowledge of PyTorch internals: DDP and FSDP, mixed precision, gradient accumulation, and profiling tools.
• Track record of building/maintaining research libraries others depend on (e.g., torch_geometric, torchaudio, torcheeg).
• Experience with data pipelines over large unstructured/multimodal datasets; familiarity with Parquet, Arrow, WebDataset, etc.
• Hands-on experiment tracking and dataset versioning tools (ClearML, W&B, MLflow).
• Ability to read papers, reimplement components, and assess loss curves.
• Debugging range from corrupted shards to silently wrong collate, etc.
• Care for well-being of people touched by this tech; thrive in high-bandwidth, collaborative env.

Responsibilities

• Orchestrate and optimize training: distributed configuration, mixed precision, checkpointing, and profiling.
• Build high-throughput data loading over TB-scale multimodal data: sharding, prefetching, caching.
• Wrangle complex data: align and synchronize multi-stream time series; dataset versioning and lineage.
• Build pipelines that derive rich features from raw recordings; versioning and reusable pipelines.
• Write well-documented libraries for datasets, models, transforms and metrics.
• Ensure runs are traceable by recording code version, config, dataset version, and environment.
• Create evaluation harnesses that run on new checkpoints and surface regressions.
• Turn research prototypes into repeatable pipelines without stifling flexibility.

Skills

Python

PyTorch internals

Distributed training

Data pipelines

Experiment tracking

Debugging

Collaboration

Research literacy

Tools

torch_geometric

torchaudio

torcheeg

torch_brain

neuralsets

Job description

Constellation is seeking a Research Engineer in San Francisco to bridge data and models, accelerating long-horizon multimodal training. You will build data pipelines, libraries, and evaluation tools while collaborating with engineering and research teams.

You will optimize distributed training, manage data versioning, and turn prototypes into repeatable pipelines that scale across GPUs. Strong Python/PyTorch skills are essential.