Unity Technologies SF

Unity Technologies SF

Machine Learning Engineer, Next-Generation Recommendation Systems

New York, New York · Internship

Sponsorship not specified$127k-$191kDetected 28 days ago
PythonMachine LearningPyTorchLLMsAgentic AIMLOpsStatisticsA/B TestingUnityAR/VRResearchCommunicationCollaboration

Stay score

odds of building a lasting career here

57Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role90
Entry-level history0
PERM / green-card track0
Lottery odds (Level III)83
Fits your clock70

Sponsors, but it's cap-subject — you still face the weighted lottery (~45% per draw at Level III). 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 I · 1×
Level I$109,8451 entry
Level II$137,7172 entries
Level III$165,5893 entries
Level IV$193,4614 entries

$10,317 more$137,717 — moves this role to Level II and 2 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 $137,717 would place this position at wage Level II. 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) · New York-Newark-Jersey City, NY-NJ. 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

Green-card filing pattern in this occupation

Context, not a finding about this posting: of this employer's 9 green-card filings in this occupation, 100% were for a worker who already held the job.

Files H-1B transfers

23 transfer filings in the last year, covering 23 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.

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About the role

  • Recommendation and ranking systems are the core of this work: predicting user value, optimizing bids, and delivering outcomes for advertisers at massive scale.
  • The frontier has shifted - large language models, reinforcement learning from human feedback, and agentic AI are reshaping what recommendation systems can do.
  • We are looking for PhD graduates who have worked at that frontier and want to bring those ideas into production systems that matter.

Responsibilities

  • Develop user understanding systems - conversion prediction, behavioral modeling, and value estimation - that operate across billions of impressions.
  • Design and run rigorous experiments using causal inference, A/B testing, and offline evaluation frameworks to measure and improve model quality.
  • Partner with engineering to bring research ideas into production, working across the full pipeline from training data to deployed model.
  • Relocation support is not available for this position
  • Our differences are strengths that enable us to support the growing and evolving needs of our customers, partners, and collaborators.

Requirements

  • Experience working with large-scale data and ML systems, whether through research or industry internships.
  • familiarity with ML frameworks such as PyTorch or TensorFlow.
  • A track record of rigorous, high-quality research - publications at top venues (NeurIPS, ICML, ICLR, KDD, RecSys, ACL, WWW, or similar) are a strong signal.
  • Industry experience in ads, recommendation, or user understanding systems (internship experience counts).
  • Hands-on experience with production ML pipelines - training at scale, feature engineering, or experimentation infrastructure.
  • Experience applying LLMs or generative models to ranking, retrieval, or structured prediction problems.
  • Familiarity with agentic AI approaches - multi-step reasoning, tool use, or human-AI collaboration frameworks.

Skills

  • Apply reinforcement learning and optimization techniques to bidding strategy, auction dynamics, and real-time ad delivery.
  • Communicate findings clearly to technical and non-technical stakeholders across engineering, product, and business teams.

Compensation

  • This range reflects the anticipated base salary for this position.

Benefits

  • We offer a wide range of benefits designed to support well-being and work-life balance.
  • Please note: Benefits eligibility, specific offerings, and coverage vary based on the country and employment status.
  • Unity also enables teams across industries like automotive, manufacturing, and healthcare to design, simulate, and collaborate in 3D - closing the gap between ideas and reality.
  • If you have a disability that means there are preparations or accommodations we can make to help ensure you have a comfortable and positive interview experience, please fill out this form to let us know.
  • Design, build, and evaluate next-generation ranking and recommendation models that incorporate LLMs, RLHF, and preference learning to improve ad relevance and user experience.

Company info

  • This posting is intended to fill an existing vacancy, and we are committed to providing applicants with updates throughout the hiring process in accordance with applicable law.
  • We are building the next generation of these systems.
  • At Unity, we want our team members to thrive.

Equal opportunity

  • equal opportunity employer.

This listing is sourced directly from Unity Technologies SF's careers page and normalized into a canonical job model.