Sr. Backend Software Engineer (Infrastructure ML Ops)

Philo, Inc.; San Francisco, New York, Cambridge (CA, NY, MA); Hybrid; USD 195,000 - 250,000; Full time

Benefits offered by this job

Health benefits

401(k) with employer match

Flexible working hours

Parental leave (up to 20 weeks)

Unlimited PTO

Annual vacation bonus

Home office + TV stipend

Job summary

Philo, Inc. is seeking an Infrastructure MLOps Software Engineer to advance our ML infrastructure in a highly distributed, cloud-based environment.

You’ll own model serving, evaluation pipelines, and observability, while scaling GPU workloads on Kubernetes and enforcing IaC practices. You will partner with engineering teams across discovery, video delivery, and analytics to ship reliable infrastructure and drive efficiency in compute and data pipelines.

Qualifications

• 8+ years of software development and infrastructure management experience.
• 4+ years of experience developing and deploying ML systems at scale.
• Experience with Linux and containerized environments (Docker).
• Experience with cloud computing environments (AWS or GCP) and configuring cloud services.

Responsibilities

• Manage the core infrastructure at Philo focusing on ML/LLM workloads.
• Design, build, and maintain cloud infrastructure components (AWS and Kubernetes).
• Scale model serving and inference infrastructure for real-time recommendations and LLM workloads.
• Manage GPU and accelerator infrastructure on Kubernetes, including cost optimization.
• Develop monitoring/observability systems for system health and model quality.
• Build and operate model evaluation infrastructure including offline/online eval pipelines.
• Maintain CI/CD systems to enable pipelines for testing and deployment.
• Promote Infrastructure as Code and collaborate across teams.

Skills

Software development

Infra management

Distributed systems

Linux

Golang

TypeScript

Python

Tools

Kubernetes

Docker

AWS

GCP

CloudWatch

Terraform

CloudFormation

Prometheus

Grafana

GitLab

Jenkins

Job description

At Philo, we’re a group of technology and product people who set out to build the future of television, marrying the best in modern technology with the most compelling medium ever invented — in short, we’re building the TV experience that we’ve always wanted for ourselves. In practice this means leveraging cloud delivery, modern tech stacks, machine learning, and hand-crafted native app experiences on all of our platforms. We aim to deliver a rock solid experience on the streaming basics, while cooking up next generation multi-screen and multi-user playback experiences.

Senior Backend Software Engineer (Infrastructure MLOps)

The infrastructure team at Philo builds the foundation for a 24/7 live and on-demand streaming product serving video to our growing subscriber base. It provides a platform that enables groups across the company – such as video delivery, product, support, and analytics – to do their best work easily and smoothly. Our systems are highly distributed, horizontally scalable and run in the cloud on Kubernetes. We continuously test and deploy our code and closely monitor the health and performance of our production environment.

We are seeking an enthusiastic Infrastructure MLOps Software Engineer to help us build and improve the ML infrastructure at Philo. We are building out ML and LLM infrastructure and this role will be pivotal in setting direction, adopting best practices, and shipping infrastructure that will be used across the company.

As a sample, the ML infrastructure supports teams like Discovery in building content recommendation models that will help our users find more favorite shows from the large Philo catalog. The engineering team is designing and developing our own agent harnesses to support coding and empowering everyone at the company to get answers across our code bases, analytics, logs, and communication tools. The video player team is using LLMs to classify ads and label our video catalog so that users have a better experience. You’ll build the serving, evaluation and platform infrastructure that these workloads depend on.

Responsibilities

• Manage the core infrastructure at Philo with a focus on supporting ML and LLM workloads. This infrastructure is relied on by a large subscriber base of Philo’s product as well as internal stakeholders across the company.

• Design, build, and maintain cloud infrastructure components (AWS and Kubernetes) using a combination of in-house technology and open source software.

• Build and scale model serving and inference infrastructure for both real-time recommendation models and LLM workloads — including model servers (e.g. NVIDIA Triton), LLM inference engines (e.g. vLLM, TGI, TensorRT-LLM), autoscaling, and latency/throughput optimization.

• Manage GPU and accelerator infrastructure on Kubernetes — scheduling, capacity and quota management, and cost optimization across on-demand and spot capacity.

• Design, build, and maintain monitoring and observability systems — spanning both system health and model quality (drift, evaluation, and online performance) — that enable Philo engineers and the support team to gain insight and to discover and debug issues.

• Build and operate model evaluation infrastructure: offline and online eval pipelines, golden/regression datasets, LLM-as-judge harnesses, A/B testing and experimentation, and feedback/ground-truth loops

• Design, build, and maintain CI/CD systems enabling Philo engineers to create pipelines to test and deploy their code.

• Establish tools, methods and best practices for other Philo engineers interfacing with the Philo ML infrastructure. Ensure reliability, security, and scalability of the Philo platform. Promote Infrastructure as Code.

• Work closely with other Philo engineers to deploy and instrument software systems.

• Drive evaluation, selection, and integration of third-party vendor systems and work closely with vendors to configure and manage them.

Qualifications

• 8+ years of software development and infrastructure management experience.

• 4+ years of experience developing and deploying ML systems at scale across training and inference

• Experience working with distributed systems and an understanding of microservices architecture principles.

• Experience with Linux and containerized (i.e. Docker) environments.

• Experience managing cloud computing environments (AWS or GCP) and configuring cloud services e.g. CloudWatch, Route 53, RDS, ElastiCache, SQS, ALB/NLB/ELB, VPC networking, IAM security.

• Experience with container orchestration platforms (i.e. Kubernetes), and familiarity with running GPU/accelerated workloads on them (e.g. NVIDIA device plugin, MIG, timeslicing).

• Strong understanding of networking and internet application protocols including, but not limited to TCP/IP, DNS, and HTTP.

• Strong understanding of network and application security principles and best practices.

• Familiarity or hands‑on experience with configuration management systems and Infrastructure as Code (e.g. Terraform, CloudFormation).

• Familiarity or hands‑on experience with Monitoring/Observability systems (e.g. Prometheus, Grafana, TICK/InfluxDB, Fluentd, ELK, Datadog).

• Familiarity or hands‑on experience with CI/CD automation systems e.g. Jenkins, Gitlab.

• Experience with relational and non‑relational databases and familiarity with modern data warehousing and querying.

• Proficient in writing, testing, and profiling software in Golang, JavaScript/TypeScript, C++, Ruby, Python or similar programming languages.

• Experience and aptitude for collaborating and communicating with internal and external stakeholders in both business and technical roles.

A strong candidate may also have one or more of these:

• Hands‑on experience optimizing inference — dynamic/continuous batching, quantization, KV‑cache management, or GPU memory tuning — for low‑latency or highthroughput serving.

• Experience with ML platform tooling: model registry and experiment tracking (e.g. MLflow, Weights & Biases), feature stores (e.g. Feast, Tecton), and pipeline/workflow orchestration (e.g. Airflow, Dagster, Kubeflow, Ray, Metaflow).

• Experience building model evaluation, monitoring, or experimentation systems — including drift detection, LLM-as-judge, or A/B testing for models.

• Experience with LLMOps: inference gateways/routers (e.g. LiteLLM), hosted model APIs (Bedrock, OpenAI, Anthropic), RAG pipelines, vector databases (e.g. pgvector, Pinecone, Weaviate), and prompt/version management.

• Experience building or operating agent harnesses or tool‑using LLM applications in production.

• Experience with data and model versioning (e.g. DVC, LakeFS) and data/labeling pipelines.

• Experience with cost management / FinOps for GPU and inference workloads

We are language agnostic, but most of our backend code is written in Golang, Ruby and TypeScript, with some C++ and Python. Our services run on Kubernetes, and we practice continuous deployment across all of our systems.

More about Philo

At Philo, we’re a company that puts people first—both our subscribers and our team. We empower our colleagues to do their best work while supporting one another in pursuing shared goals. We value pragmatism, pride in our work, and passion, with transparency and openness as fundamental parts of our culture.

We’re committed to diversity, equity, inclusion, and accessibility as we grow the Philo team and shape the future of TV. We believe that a diverse range of voices and perspectives enables us to innovate faster and create the best experiences for our subscribers. Philo is proud to be an Equal Opportunity Employer. We’re committed to supporting every candidate and employee. If you need an accommodation at any stage of the process, please email [e-mail] and we’ll work with you to meet your needs.

Philo offers 70+ top-rated networks, including AMC, BET, CMT, Comedy Central, Discovery Channel, Food Network, Hallmark Channel, HGTV, HISTORY, Investigation Discovery, Lifetime, MTV, Nickelodeon, OWN, VH1, We TV, and more. It also includes all the groundbreaking originals and blockbuster movies available with AMC+ and access to HBO Max Basic With Ads and discovery+.

Our extensive library boasts over 85,000 titles, and our unlimited DVR allows users to save their favorite shows and movies for up to a year, skipping ads for a seamless viewing experience. Stream on up to three devices simultaneously, whether on your phone, tablet, laptop, or TV using Roku, Apple TV, Fire TV, Samsung TV, Android TV, Vizio TV, or Chromecast.

Philo is headquartered in San Francisco, with offices in New York City and Cambridge, MA. Our leadership team includes alums from HBO, Tubi, and Meraki, and is backed by NEA and industry partners like Warner Brothers Discovery, Paramount, AMC, and A& E.

Join us at Philo and be part of a team that’s shaping the future of TV!

Status

Full-time

Location

San Francisco, CA, Brooklyn, NY, Cambridge, MA, or remote

Compensation

Includes annual salary, company stock options, and health benefits. Salary is determined by experience and location:

• San Francisco, New York City: $195K - $250K

• Boston, DC Metro, Los Angeles, Seattle: $185K - $240K

• Denver, Atlanta, Austin, Las Vegas, Sacramento, Chicago: $175K - $230K

• Texas, Florida: $165K - $220K

We value a diverse and inclusive workplace and we welcome people of different backgrounds, experiences, skills, and perspectives. Philo is an equal opportunity employer. We believe that everyone does their best work when they are supported by each other and the company, and we offer a generous set of benefits to make sure the Philo team is happy and healthy. Here is a sampling of the benefits we offer our team:

• Full health, dental and vision coverage for you and your family

• 401(k) plan with employer contributions (we match 100% of deferrals up to 3% of pay and 50% of the next 2% of pay)

• Flexible working hours

• Up to 20 weeks of fully paid parental leave

• Unlimited paid time off for vacation and sick leave

• $2,000 annual vacation bonus (we pay you to take a two week vacation)

• $5,250 annually for professional development and educational assistance

• $1,250 annual home office + TV stipend during first year of employment ($250 annually thereafter)

• $500/month ($6,000/year) bonus for employees who commit to working at least 3 days per week in our offices, plus generous commuter benefits ($315/month towards transit, rideshare, bike rental, or parking at our HQ office in San Francisco)

• Free Gympass subscription — an all-in-one corporate benefit that gives employees the largest selection of gyms, studios, classes, training and wellness apps

• Dog-friendly office

• And much more!

For California Residents: Philo’s CCPA Notice at Collection – Employees, Applicants, Owners, Directors, Officers and Contractors

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Use of Automated Tools in Our Hiring Process

We use software tools, including our applicant tracking system (Ashby), to help manage applications for this role. Automated features (i) assess the skills, experience, and qualifications relevant to the role as described in this posting, and (ii) flag applications that appear fraudulent, duplicative, or not genuinely submitted by the applicant.

A summary of the most recent independent bias audit of the automated employment decision tool used in this process, including the audit date and data source, is available here: Bias Audit Summary.

You may request that a member of our team review a decision through a process that does not rely on the automated tools, or request a reasonable accommodation, by emailing [e-mail]. We will not retaliate against you for exercising your rights.

Information about the type and source of the data our tools collect, our data retention policy, and how these tools were used in evaluating your application is described in our Privacy Notice – Employees,Applicants, Officers, Directors, and Contractors, or available by emailing [e-mail].

Depending on your state of residence, you may have additional rights, including to an explanation of an adverse decision, correction of inaccurate data, and meaningful human review. To exercise these or ask whether they apply, email [e-mail].