Research Engineer

Kalpa Labs; San Francisco (CA); Hybrid; USD 150,000 - 230,000; Full time

Job summary

Kalpa Labs in San Francisco, CA is hiring a Founding ML Research Engineer to own the end-to-end development of Generalist Audio Models. You’ll work across data pipelines, model pre-training, post-training, and evaluation to push research ideas toward production rapidly.

You will build scalable ML systems, distillation and training pipelines, and collaborate with a small team with ample compute to turn ideas into shipped features in weeks.

Qualifications

• Industry/Academia experience pre-training / post-training large neural networks; speech/audio is a plus but not required, language/vision experience is also relevant.
• Strong ML systems and engineering depth (distributed training, performance, reliability).
• Comfort operating in ambiguity: you can spec, build, debug, and ship.
• A hunger to always ask - what would the next frontier look like?

Responsibilities

• Research better multi-modal architectures & codecs that are efficient across both spoken speech & general audio.
• Post-train audio models to have LLM like instruction following & in-context learning but over both text and audio.
• Build large-scale speech model pre-training and post-training (SFT/RLHF-style, distillation, preference optimization, etc.).
• Build scalable data + compute pipelines: dataset curation, filtering, mixing, tokenization/feature pipelines, evaluation harnesses.
• Look at lots of data & hear lots of audio.

Skills

Multi-modal ML

Audio processing

Distributed training

Data pipelines

Research mindset

Job description

San Francisco, CA, US Full-time Will sponsor 3+ years Machine Learning

We’re hiring a Founding ML Research Engineer to work work through the full stack across data, pre-training, post-training & evals for training Generalist Audio Models. You’ll work through the entire stack with small team, tons of compute, high autonom, and see your research ideas making it to production within a week(s).

What you’ll do

• Research better multi-modal architectures & codecs that are efficient across both spoken speech & general audio.
• Post-train audio models to have LLM like instruction following & in-context learning but over both text and audio.
• Build large-scale speech model pre-training and post-training (SFT/RLHF-style, distillation, preference optimization, etc.).
• Build scalable data + compute pipelines: dataset curation, filtering, mixing, tokenization/feature pipelines, evaluation harnesses.
• Look at lots of data & hear lots of audio.

What we’re looking for

• Industry/Academia experience pre-training / post-training large neural networks; speech/audio is a plus but not required, language/vision experience is also relevant.
• Strong ML systems and engineering depth (distributed training, performance, reliability).
• Comfort operating in ambiguity: you can spec, build, debug, and ship.
• A hunger to always ask - what would the next frontier look like?