Unity Technologies SF

Unity Technologies SF

Senior Machine Learning Engineer, ML Infrastructure- Online

Olympia, Washington · Senior

No sponsorship$187k-$243kDetected 44 days ago
PythonDistributed SystemsKubernetesMachine LearningTensorFlowPyTorchAirflowMLOpsAI OrchestrationA/B TestingUnityAR/VRCommunication

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odds of building a lasting career here

61Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role90
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.

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H-1B wage level

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

Level IV · 4×
Level I$96,2621 entry
Level II$113,6512 entries
Level III$131,0403 entries
Level IV$148,4294 entries

This range already reaches Level IV — the maximum four lottery entries.

DOL prevailing wage, 2026-27 wage year · Software Developers (15-1252) · Olympia-Lacey-Tumwater, WA. 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

  • You will work closely with ML engineers, platform teams, and product stakeholders to ensure models can be deployed, scaled, monitored, and iterated on efficiently.
  • You will play a key role in shaping how models are packaged, served, validated, monitored, and optimized in production environments.

Responsibilities

  • Design and operate large-scale online inference infrastructure that serves production ML models with low latency and high reliability, such as PyTorch, Triton Inference Server, Kubernetes, GKE, Ray, or similar distributed serving frameworks.
  • Develop infrastructure that supports distributed training workflows using technologies such as Pytorch, Ray Data, and Ray Train, etc.
  • Optimize model performance through model compilation, GPU/CPU utilization improvements, request scheduling, kernel fusion, and runtime-level tuning.
  • Partner closely with ML engineers to support faster model iteration while maintaining production safety, scalability, and cost efficiency.
  • Lead architectural improvements that make the online ML platform more robust, user-friendly, scalable, and cost-efficient.
  • 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 with model serving frameworks such as NVIDIA Triton Inference Server, TorchServe, Ray Serve, TensorFlow Serving, or similar systems.
  • Experience optimizing inference workloads using techniques such as dynamic batching, model compilation, quantization, GPU acceleration, GPU kernel optimization, caching, or runtime tuning.
  • Strong experience with distributed systems, Kubernetes, autoscaling, service reliability, and production observability.
  • Strong programming skills in Python, with practical experience working on production ML systems and high-scale services.
  • Experience with PyTorch and modern model deployment workflows, including model packaging, validation, and serving lifecycle management.
  • Experience designing infrastructure for safe model rollout, canary testing, A/B experimentation, and automated rollback.
  • Strong systems thinking, with the ability to reason about latency, throughput, reliability, scalability, and cost tradeoffs in online systems.

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.
  • Improve observability of ML systems through latency, throughput, error-rate, cost, saturation, and model-health monitoring.

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 seeking a Senior ML engineer to design and evolve Unity Vector's online model inference platform.
  • At Unity, we want our team members to thrive.

Equal opportunity

  • equal opportunity employer.

Visa & Work Authorization

  • Work visa/immigration sponsorship is not available for this position

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