Edison Scientific

Edison Scientific

AI Engineer - Forward Deployed

San Francisco, CA · Mid

Sponsorship not specified$200k-$350kDetected 209 days ago
TypeScriptPythonMachine LearningLLMsAgentic AIResearchCommunicationProblem Solving

Stay score

odds of building a lasting career here

59Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70

Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.

Lottery odds assume a STEM candidate.

Personalize to your clock →

H-1B wage level

the lottery is wage-weighted — each level is one more entry

Level III · 3×
Level I$137,0931 entry
Level II$165,1312 entries
Level III$193,1493 entries
Level IV$221,1874 entries

$21,187 more — $221,187 — moves this role to Level IV and 4 lottery entries. That figure is inside the range the employer already advertised.

Based on the DOL prevailing wage for this occupation and worksite, a base salary of $221,187 would place this position at wage Level IV. That figure is within the posted range, and I'd like to target it. This role classifies under "Software Developers" for prevailing-wage purposes.

DOL prevailing wage, 2026-27 wage year · Software Developers (15-1252) · San Francisco-Oakland-Fremont, CA. Wage level is derived by USCIS from the offered wage, occupation and worksite; the occupation shown is inferred from the job title.

Employer immigration record

from this employer's Department of Labor filings

Files H-1B transfers

4 transfer filings in the last year, covering 4 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.

Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.

Community outcomes

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About the role

  • Scientific discovery moves too slowly, and autonomous AI agents are how we intend to fix that.

Responsibilities

  • Be embedded at a client site, understand their research workflows deeply, and take responsibility for maximizing the value Edison delivers to that organization.
  • Design, implement, and iterate on production-ready integrations in fast cycles, often owning features end-to-end from architecture to rollout.

Requirements

  • 4+ years of professional software engineering experience, with a track record of shipping production-grade software in fast-paced or ambiguous environments.
  • Proficiency in Python and/or TypeScript/JavaScript.
  • Experience in life sciences, pharma, biotech, or scientific R&D - either as a researcher, consultant, or engineer working in these environments.
  • Experience working with AI/ML systems, LLM-powered applications, or agent architectures.
  • A science degree or research experience alongside your engineering skills.
  • Comfortable operating independently, making decisions with incomplete information, and driving outcomes without close oversight.
  • Strong problem-solving skills and comfort navigating complex systems, debugging issues in real time, and owning features end-to-end.
  • Strong communication and relationship-building skills - able to earn trust with scientists, executives, and engineering teams.
  • Willingness to learn new scientific domains quickly and adapt to rapidly evolving priorities.
  • Bonus points for:
  • Background as a technical founder, forward-deployed engineer, or management consultant.

Compensation

  • $200,000 - $350,000
  • Competitive salary and equity
  • Full healthcare coverage - we pay 100% of premiums for you and your dependents

Benefits

  • Support for growing families, including a yearly new parent stipend and fertility coverage through Carrot
  • $300 health and wellness benefit

This listing is sourced directly from Edison Scientific's careers page and normalized into a canonical job model.