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About Liquid AI Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there. The Opportunity Our Data team powers Liquid Foundation Models across pre-training, vision, audio, and emerging modalities. Public data sources are plateauing. Model performance increasingly depends on purpose-built datasets. We need ML-minded engineers who can collect, filter, and synthesize high-quality data at scale. We treat data as a research problem, not an infrastructure problem. Our engineers run experiments, design ablations, and measure how data decisions move model quality. We will match you to the team where you can grow the fastest and have the most impact: pre-training, post-training RL, vision-language, audio, or multimodal. While San Francisco and Boston are preferred, we are open to other locations. What We're Looking For We need someone who: Thinks like a researcher, ships like an engineer: We need people who form hypotheses, run experiments, and measure results. Our engineers understand deep-theoretical research, and our researchers ship production systems. Learns fast and adapts: We work across modalities that evolve weekly. We need people who pick up new domains quickly and thrive with ambiguity. Obsesses over data quality: We believe data quality is non-negotiable. Filtering, deduplication, augmentation, and evaluation are first-class concerns for our team, not afterthoughts. Solves problems independently: Our data engineers sit within training groups (pre-training and multimodal). We collaborate closely, but we expect ownership and self-direction. The Work Build and maintain data processing, filtering, and selection pipelines at scale Create pipelines for pretraining, midtraining, SFT, and preference optimization datasets Design synthetic data generation systems using LLMs, structured prompting, and domain-specific generators Design and run evaluations and ablations to measure dataset's impact on model performance Monitor public datasets across text, vision, and audio domains Collaborate with pre-training, vision, and audio teams on modality-specific data needs Must-have Desired Experience Strong Python skills with the ability to quickly comprehend problems and translate them into clean, working code Solid ML fundamentals: experience training, evaluating, and iterating on models (PyTorch preferred) Track record of learning new technical domains quickly 3+ years relevant experience with an M.S., or 1+ year with a Ph.D. (5+ years with a B.S.) Nice-to-have Experience with synthetic data generation, data curation, or ML evaluation (designing evals, benchmarking, measuring data and model quality) Experience with LLMs, VLMs, computer vision, or audio data pipelines Open-source contributions or publications at NeurIPS, ICML, ICLR, or CVPR What Success Looks Like (Year One) You own a critical data pipeline end-to-end for one of our modalities You have built or improved data systems that measurably moved model performance You have identified and integrated at least one external dataset that moved the needle What We Offer Impact at scale: Your pipelines directly determine model quality across all of Liquid's foundation models. Compensation: Competitive base salary with equity in a unicorn-stage company Health: We pay 100% of medical, dental, and vision premiums for employees and dependents Financial: 401(k) matching up to 4% of base pay Time Off: Unlimited PTO plus company-wide Refill Days throughout the year
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.