Sieve — Forward Deployed Engineer

davidjoseph-co; San Francisco (CA); On-site; USD 150,000 - 250,000; Full time

Benefits offered by this job

401(k)

Full health insurance

Meals covered

Choice of snacks

Ubers covered home

Competitive equity

Job summary

davidjoseph-co is seeking a Forward Deployed Engineer in San Francisco, CA. In this role, you will manage end-to-end dataset projects, building models and data pipelines while collaborating with customers to meet their dataset needs.

The ideal candidate will have 1–3 years of experience, strong Python skills, and the ability to navigate between research and production environments efficiently. Attractive benefits include full health insurance and a 401(k).

Qualifications

• Strong Python developer with hands-on experience building custom algorithms, model workflows, or large-scale data pipelines.
• Comfortable working directly with customers or external teams to translate ambiguous needs into technical systems.
• 1–3 years of experience shipping technical work in a startup or high-velocity environment.

Responsibilities

• Work directly with customers to translate ambiguous dataset needs into technical systems and delivery timelines.
• Build custom algorithms, models, and large-scale data pipelines spanning various domains.
• Optimize performance through pre/post-processing, parallelism, and evaluation loops.

Skills

Python development

Custom algorithms

Large-scale data pipelines

Customer interaction

Tools

PyTorch

Job description

Sieve — Forward Deployed Engineer

Type: Full-time | On-site | San Francisco, CA Compensation: $150,000 – $250,000 + competitive equity Experience: 1 – 3 years Hiring count: 4 (hiring multiple) Visa sponsorship: Yes — H-1B, OPT Tech stack: Python, PyTorch (or similar ML frameworks), large-scale data pipelines

About Sieve

Sieve is an AI research lab focused exclusively on video data. Video makes up ~80% of internet traffic and is the dominant medium across creativity, communication, gaming, AR/VR, and robotics — but progress in video modeling has been bottlenecked by access to high-quality training data.

Sieve combines exabyte-scale video infrastructure, novel video understanding techniques, and dozens of diverse data sources to build datasets that push the frontier of video modeling — with precision, quality, and speed that has earned the trust of frontier AI labs, Fortune 100 companies, and fast-growing generative AI startups. Beyond video, the team works on audio and multimodal data processing for AI training and evaluation.

Seed-stage, founded 2022, San Francisco. Website: sievedata.com

About This Role

You'll own end-to-end dataset projects for customers — from untangling ambiguous requirements through shipping production systems that find, generate, filter, transform, evaluate, and package high-quality datasets at scale. This is a high-agency role working directly with customers and internal teams, combining research prototypes with reliable production pipelines. You'll ship fast, move between technical domains within each project, and own customer outcomes directly.

What You'll Own

• Work directly with customers to translate ambiguous dataset needs into concrete technical systems and delivery timelines


• Build custom algorithms, models, and large-scale data pipelines spanning computer vision, audio processing, text processing, and metadata analysis


• Move between research prototypes and production systems, using models and APIs creatively to solve customer problems


• Break down customer-level goals into the models, heuristics, infrastructure, and QA steps needed to deliver


• Optimize performance through pre/post-processing, parallelism, inference optimization, fine-tuning, and evaluation loops

Must-Have

1. Strong Python developer with hands-on experience building custom algorithms, model workflows, or large-scale data pipelines


2. Comfortable working directly with customers or external teams to translate ambiguous needs into technical systems


3. Deep intuition for dataset quality, filtering, labeling, evaluation, and edge cases


4. Able to move quickly between research prototypes and reliable production systems without creating brittle code


5. 1–3 years of experience shipping technical work in a startup or high-velocity environment

Nice-to-Have

• Experience building custom algorithms or ML workflows for production video, audio, or multimodal data


• Hands-on work with large-scale data pipelines at scale


• Background with PyTorch or similar ML frameworks in production


• Active contributor to open source projects


• Early hire experience at a startup

Benefits & Perks

• 401(k)


• Full health insurance


• Breakfast, lunch, and dinner covered


• Choice of snacks


• Ubers covered home


• Competitive equity