ML Platform Engineer, Distributed Training

Remote

Live, first seen by us 15 days ago

Pay: USD 159,300–219,245/year — pay source
Salary 159,300 – 219,245 USD per year Compensation & Benefits:
Read the full posting
Work setting: Remote — work setting source
Listed location: Remote
Work setup in the employer’s posting: remote
From the employer’s posting
Employment: Employment terms need review — employment source
Employment type Full Time or Contract
Read the full posting

What you’ll work on

Full posting

Diagnose and fix GKE training platform failures at the root, not by restarting
Run, scale and debug multi-node distributed training with PyTorch and Ray Train
Profile training jobs and remove bottlenecks across compute, memory, input and interconnect
Tune multi-node GPU networking (RDMA/GPUDirect, NCCL) for training throughput
Design and run cluster benchmarks for throughput, scaling efficiency and cost per run

From the employer’s posting

Diagnose and fix platform failures at the root (for example a controller that keeps running out of memory) rather than restarting them.

Run, scale and debug multi-node distributed training with PyTorch (FSDP, DeepSpeed, Megatron or NeMo-style) and Ray Train.

Profile jobs and remove bottlenecks across compute, memory, input pipelines and interconnect.

Tune multi-node GPU networking (RDMA/GPUDirect, NCCL) for training throughput.

Design and run performance benchmarks for the cluster and training stack: throughput, scaling efficiency, cost per run.

What you’ll bring

All qualifications

Core experience

5+ years of engineering experience, with recent hands-on work on GPU training infrastructure and large-scale training.
Strong Python software engineering: production-quality code, tests and code review.

Qualification wording

5+ years of engineering experience, with recent hands-on work on GPU training infrastructure and large-scale training.

Strong Python software engineering: production-quality code, tests and code review. Coding is central to this role, not an afterthought.

Benefits in the posting

Full benefits wording

This role is eligible for our competitive salary and comprehensive benefits package to support your well-being:
Comprehensive Health Insurance
Paid Leave (Vacation/PTO)
Paid Holidays
Sick Leave
Parental Leave
Bereavement Leave
Employee Referral Bonuses
Important: All roles are subject to standard hiring verification practices, which may include background checks, employment verification, and other relevant checks.
EEO and Accommodations:

From the employer’s posting.

About Egen

Egen is a fast-growing and entrepreneurial company with a data-first mindset.

In the employer’s words · Read in context

Apply at Egen

Posting record

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Status in our records: Live
First seen by us: Oct 20, 202615 days ago
Recorded sightings: 15
Last seen by us: Nov 4, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Pay

In line with similar jobs

$159K–$219K against $166K–$240K, the middle half of what similar US postings state.

From 1,279 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 15

Similar jobs usually come down between day 6 and day 69.

From 4,296 similar postings that came down. Coming down is not always a hire.

Egen is hiring

6 open jobs

Open jobs by the week we first saw them, last 12 weeks.

ML Engineer hiring is picking up
Takes jobs down at a usual pace

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

Python
Kubernetes
PyTorch
GCP

Where the posting names them

Your Technical Toolkit: - Core Languages: Mastery of Python and shell scripting - AI/LLM Ecosystem: Extensive experience with Google Gemini, GPT-4, or LLaMA; deep knowledge of Prompt Engineering and Fine-tuning

- 5+ years of engineering experience, with recent hands-on work on GPU training infrastructure and large-scale training. - Strong Python software engineering: production-quality code, tests and code review. Coding is central to this role, not an afterthought. - GKE and GPU scheduling: Kubernetes on Google Cloud with GPU node pools, job queueing (Kueue or similar), and capacity and quota management.

- Strong Python software engineering: production-quality code, tests and code review. Coding is central to this role, not an afterthought. - GKE and GPU scheduling: Kubernetes on Google Cloud with GPU node pools, job queueing (Kueue or similar), and capacity and quota management. - Distributed PyTorch and Ray Train: has personally run and debugged multi-node training jobs.

Distributed training - Run, scale and debug multi-node distributed training with PyTorch (FSDP, DeepSpeed, Megatron or NeMo-style) and Ray Train. - Profile jobs and remove bottlenecks across compute, memory, input pipelines and interconnect.

- GKE and GPU scheduling: Kubernetes on Google Cloud with GPU node pools, job queueing (Kueue or similar), and capacity and quota management. - Distributed PyTorch and Ray Train: has personally run and debugged multi-node training jobs. - RDMA and GPU networking: GPUDirect/RDMA, NCCL tuning, multi-node GB200 (or H200) topology.

About Egen: Egen is a fast-growing and entrepreneurial company with a data-first mindset. We bring together the best engineering talent working with the most advanced technology platforms, including Google Cloud and Salesforce, to help clients drive action and impact through data and insights. We are committed to being a place where the best people choose to work so they can apply their engineering and technology expertise to envision what is next for how data and platforms can change the world for the better. We are dedicated to learning, thrive on solving tough problems, and continually innovate to achieve fast, effective results. If this describes you, we want you on our team. Want to learn more about life at Egen? Check out these resources in addition to the job description.

- Data & Search: Expertise in Vector Databases (Vertex AI Vector Search, pgvector, etc.) and implementing Semantic Search - Infrastructure: Hands-on experience with Google Cloud (Vertex AI) and building scalable software architectures - Frameworks: Proficiency in LangChain, LlamaIndex, or similar orchestration layers

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About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025: $140,300
Expected change, 2025 to 2035: 21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

About Egen:

Egen is a fast-growing and entrepreneurial company with a data-first mindset. We bring together the best engineering talent working with the most advanced technology platforms, including Google Cloud and Salesforce, to help clients drive action and impact through data and insights. We are committed to being a place where the best people choose to work so they can apply their engineering and technology expertise to envision what is next for how data and platforms can change the world for the better. We are dedicated to learning, thrive on solving tough problems, and continually innovate to achieve fast, effective results. If this describes you, we want you on our team.

Want to learn more about life at Egen? Check out these resources in addition to the job description.

Meet Egen

Life at Egen

Culture and Values at Egen

Career Development at Egen

Benefits at Egen

About the opportunity

We're looking for an engineer who is equally at home running a GPU training platform and the large-scale model training that runs on it. You'll be embedded with a healthcare and life-sciences AI client that trains foundation models on a large GKE-based H200 and GB200 cluster. The client is reorganizing so that its foundation-model platform and its agent platform teams work more closely together.

The role centres on the training stack and the scientists who use it. Solid infrastructure knowledge and experience are required, but the client already has strong SRE and infrastructure capacity, so running the cluster isn't the focus: you'll partner with that team and understand the training workloads well enough to tune, benchmark, and debug them end to end. You'll work alongside other Egen engineers and client scientists, and you'll own your area outright.

What You Will Do:

Training platform

Work with the client's infrastructure team on the GKE training platform (GPU node pools, job queueing and scheduling, quota and capacity) from the training workload's side.
Diagnose and fix platform failures at the root (for example a controller that keeps running out of memory) rather than restarting them.
Tune multi-node GPU networking (RDMA/GPUDirect, NCCL) for training throughput.

Distributed training

Run, scale and debug multi-node distributed training with PyTorch (FSDP, DeepSpeed, Megatron or NeMo-style) and Ray Train.
Profile jobs and remove bottlenecks across compute, memory, input pipelines and interconnect.

Benchmarking and evals

Design and run performance benchmarks for the cluster and training stack: throughput, scaling efficiency, cost per run.
Build and maintain model evaluation harnesses so changes to the platform or training recipes are measured, not assumed.
Instrument training and the platform with telemetry and monitoring, and keep end-to-end pipeline tests running against what users actually run.

Ownership on the client team

Treat the platform as your own: proactively find issues, plug gaps and propose solutions before anyone asks, keep the team informed of status and risks, and look after the scientists who use it.
Explain and defend your design decisions to client engineers and scientists in detail.
Stay effective while the client's priorities and team structure change.

Your Technical Toolkit:

Core Languages: Mastery of Python and shell scripting
AI/LLM Ecosystem: Extensive experience with Google Gemini, GPT-4, or LLaMA; deep knowledge of Prompt Engineering and Fine-tuning
Data & Search: Expertise in Vector Databases (Vertex AI Vector Search, pgvector, etc.) and implementing Semantic Search
Infrastructure: Hands-on experience with Google Cloud (Vertex AI) and building scalable software architectures
Frameworks: Proficiency in LangChain, LlamaIndex, or similar orchestration layers
Mindset: A strong software engineering foundation—you write clean, maintainable code and understand the full SDLC

Basic Qualifications:

5+ years of engineering experience, with recent hands-on work on GPU training infrastructure and large-scale training.

Strong Python software engineering: production-quality code, tests and code review. Coding is central to this role, not an afterthought.

GKE and GPU scheduling: Kubernetes on Google Cloud with GPU node pools, job queueing (Kueue or similar), and capacity and quota management.

Distributed PyTorch and Ray Train: has personally run and debugged multi-node training jobs.

RDMA and GPU networking: GPUDirect/RDMA, NCCL tuning, multi-node GB200 (or H200) topology.

Benchmarking and evals: has designed performance benchmarks and model evaluations, and used them to make decisions.

Deep enough understanding to explain how and why a system works, without leaning on AI coding tools to carry the reasoning.

A track record of visible ownership: present, communicative, and accountable on a demanding team.

Personal Attributes:

Nice to have

Healthcare or life-sciences data (genomics, clinical, biomedical), and working under HIPAA.

Observability and telemetry for training platforms: metrics, tracing, alerting, end-to-end pipeline testing.

This position may be hired at multiple levels. Final leveling is determined during the interview process based on a candidate's experience, skills, and interview outcomes, and the applicable salary range will align with the final level assigned.

Compensation is determined based on factors including experience, expertise, interview performance, and geographic location, where applicable.

Salary

159,300 – 219,245 USD per year

Compensation & Benefits:

This role is eligible for our competitive salary and comprehensive benefits package to support your well-being:

- Comprehensive Health Insurance

- Paid Leave (Vacation/PTO)

- Paid Holidays

- Sick Leave

- Parental Leave

- Bereavement Leave

- 401 (k) Employer Match

- Employee Referral Bonuses

Check out our complete list of benefits here - >https://egen.ai/people/#benefits

Important: All roles are subject to standard hiring verification practices, which may include background checks, employment verification, and other relevant checks.

EEO and Accommodations:

Egen is an equal opportunity employer and is committed to inclusion, diversity, and equity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veterans’ status, or any other characteristic protected by federal, state, or local laws. Egen will also consider qualified applications with criminal histories, consistent with legal requirements. Egen welcomes and encourages applications from individuals with disabilities. Reasonable accommodations are available for candidates during all aspects of the selection process. Please advise the talent acquisition team if you require accommodations during the interview process.

Employment type

Full Time or Contract

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