Senior Machine Learning Engineer, ML Infrastructure- Online

Unity Technologies SF; United States; On-site; USD 210,000 - 273,000; Full time

Benefits offered by this job

Comprehensive health, life, and disabl

Commute subsidy

Employee stock ownership

Competitive retirement plans

Generous vacation and personal days

Parental leave support

Office snacks

Job summary

Unity Technologies SF is seeking a Senior ML Engineer to design and evolve Unity Vector's online model inference platform. You will build reliable infrastructure for serving ML models in production, optimize inference performance, and enable safe experimentation across high-traffic online systems.

You will collaborate with ML engineers and platform teams to ensure scalable deployment, monitoring, and iteration of models, shaping packaging, validation, and deployment automation in production

Qualifications

• Experience operating production-grade online ML inference systems (e.g., Triton, TorchServe, Ray Serve, TensorFlow Serving).
• Strong Python programming experience in production ML systems and high-scale services.
• Experience with distributed systems, Kubernetes, autoscaling and observability.
• Experience designing infrastructure for safe model rollout, canary testing and AB testing.

Responsibilities

• Design and operate large-scale online inference infrastructure for production ML models with low latency and high reliability.
• Develop infra for distributed training workflows using PyTorch, Ray Data, and Ray Train.
• Integrate ML pipelines with workflow systems like Flyte and Airflow to enable multi-stage training.
• Optimize model performance via compilation, GPU/CPU utilization, scheduling, and runtime tuning.
• Improve observability including latency, throughput, errors, and cost monitoring.
• Collaborate with ML engineers to accelerate model iteration while preserving safety and scale.
• Lead improvements for packaging, validation, deployment automation, and reproducibility.

Skills

ML infra

Python

Kubernetes

PyTorch

Model serving

Canary testing

Latency optimization

Tools

Triton Inference Server

TorchServe

Ray Serve

TensorFlow Serving

Kubernetes

Job description

The Role

We are seeking a Senior ML engineer to design and evolve Unity Vector's online model inference platform. This role focuses on building reliable infrastructure for serving machine learning models in production, optimizing inference performance, and enabling safe, efficient experimentation across high-traffic online systems.

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.

This role requires strong systems thinking, deep experience with production ML infrastructure, and the ability to drive architectural improvements across teams.

What you’ll be doing

• 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.
• Integrate ML pipelines with workflow orchestration systems (e.g., Flyte, Airflow, or similar) to enable reliable multi-stage training workflows
• Optimize model performance through model compilation, GPU/CPU utilization improvements, request scheduling, kernel fusion, and runtime-level tuning.
• Improve observability of ML systems through latency, throughput, error-rate, cost, saturation, and model-health monitoring.
• Partner closely with ML engineers to support faster model iteration while maintaining production safety, scalability, and cost efficiency.
• Improve the reliability and reproducibility of model serving workflows, including model packaging, artifact validation, compatibility testing, and deployment automation.
• Lead architectural improvements that make the online ML platform more robust, user-friendly, scalable, and cost-efficient.

What we’re looking for

• Experience building and operating production‑grade online ML inference systems, such as NVIDIA Triton Inference Server, TorchServe, Ray Serve, TensorFlow Serving, or similar systems.
• 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.
• Proven ability to lead technical direction and influence architectural decisions across teams without formal authority.

Additional information

• Zone A: $210,300 - $273,400
• Zone B: $187,200 - $243,300
• Zone C: $165,600 - $215,200

This range reflects the anticipated base salary for this position. Beyond base salary, this role may be eligible for equity awards and participation in our company incentive plans (such as annual discretionary bonuses or sales commissions). The final offer amount will depend on several factors, including geographic location and the candidate's relevant experience, professional background, and skill set.

Benefits

At Unity, we want our team members to thrive. 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. While specific benefits vary, here are some of the ways we strive to take care of our eligible team members globally:

• Comprehensive health, life, and disability insurance
• Commute subsidy
• Employee stock ownership
• Competitive retirement/pension plans
• Generous vacation and personal days
• Support for new parents through leave and family-care programs
• Office food snacks
• Mental Health and Wellbeing programs and support
• Employee Resource Groups
• Global Employee Assistance Program
• Training and development programs
• Volunteering and donation matching program

Life at Unity

Unity [NYSE: U] is the world’s leading game engine, powering play for more than 3 billion consumers each month. The top mobile games in the world, the most played PC indie titles, the most innovative console games, and virtually all of the top XR and Web Games are developed, deployed, a