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About This Role
Location
:
San Jose
Team
:
Technology
Employment Type
:
Regular
Job Code
:
A56673A
Responsibilities
About the team
Networking brings together innovative ideas and technologies from network architecture, software defined networking (SDN), network virtualization, switch software and hardware co\-design, and high\-speed networking, to create hyper\-scale data\-center networking solutions that power several of the most popular apps of the world such as Douyin and TikTok which serve hundreds of millions of users around the globe.
Network Observation team is committed to building a world\-leading hyperscale data center network infrastructure that supports hundreds of millions of users' real\-time access and explosive growth of massive data volumes. We believe that the next generation of network operations will be fundamentally powered by artificial intelligence technologies, particularly Large Language Models (LLMs).
We are seeking a passionate development engineer who combines deep networking expertise with innovative AIOps capabilities to join us in defining and building "autonomous" data center networks. Together, we will transform network operations from a reactive "firefighting" mode into a proactive, data\-driven intelligent ecosystem with predictive and self\-healing capabilities.
We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth.
Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.
Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis \- we encourage you to apply early.
Responsibilities:
As a core member of our team, you will collaborate closely with our NetOps, SRE, and platform engineering teams to tackle the complexities of one of the world's largest data center networks. You will design and implement a closed\-loop AIOps for NetWork platform, covering:
- Build a Panoramic Network Observability Platform: Develop a streaming telemetry data pipeline for both physical and virtual networks, integrating multi\-source data from gNMI, Netconf, IPFIX/NetFlow, and SNMP to provide a high\-quality, real\-time data foundation for AIOps.
- Develop an Intelligent Diagnostics and Root Cause Analysis System: Apply machine learning and deep learning algorithms to perform anomaly detection, correlation analysis, and intelligent noise reduction on massive volumes of network metrics, logs, and events. Swiftly pinpoint root causes of failures across the entire stack, from optical transceivers and switch hardware to protocol adjacencies and application traffic.
- Explore Innovative Applications of LLMs and Agents:
- Intelligent Operations Assistant: Build a conversational chatbot powered by Retrieval\-Augmented Generation (RAG) that understands natural language queries, automatically queries knowledge bases and monitoring data, and provides precise troubleshooting guidance and network status reports.
- Automated Remediation and Smart Runbooks: Train operational Agents to safely and controllably invoke network change tools and APIs. Empower them to autonomously generate, recommend, or even execute remediation plans and emergency runbooks based on their understanding of failure scenarios.
- Establish Capacity and Risk Prediction Capabilities: Forecast network capacity bottlenecks, high\-risk links, and "sub\-healthy" devices based on historical data and business growth models, enabling proactive scaling and preventative maintenance.
- Forge a Rock\-Solid Engineering System: Adhere to engineering best practices to design and develop a highly available and scalable AIOps platform. Guarantee the stability and performance of the entire pipeline, from data collection and model training to online inference and automated closed\-loop actions.
Qualifications
Minimum Qualifications:
- Individuals who are completing or have recently completed a Bachelor's or Master's degree in Computer Science or a related discipline.
- Deep understanding of data center network architectures (e.g., Spine\-Leaf Fabric), and proficiency in key protocols such as EVPN/VXLAN and BGP/OSPF. In\-depth knowledge of the Linux network stack is essential.
- Mastery of Golang or Python with outstanding coding and system design abilities. Familiarity with modern software development workflows, including microservices, containerization (Docker/Kubernetes), and CI/CD.
- Practical experience in one or more of the following areas is highly desirable:
- Big Data Processing: Familiarity with Kafka, Flink, ClickHouse/TSDB, and experience building real\-time data pipelines and analytics systems.
- Observability Technologies: Experience with Prometheus/OpenTelemetry, graph databases (e.g., Neo4j), and developing alert and event platforms.
Preferred Qualifications:
- Experience in operating or developing for hyperscale (100,000\+ servers) data center networks.
- Proven experience leading or making significant contributions to an LLM/Agent\-based intelligent operations project with measurable business impact.
- Active contributions to open\-source communities such as SONiC, P4/PINS, eBPF, Prometheus, or OpenTelemetry.
- In\-depth research or practical experience in high\-performance networking (RDMA/RoCE), SmartNICs (NIC Offload), or DPDK/eBPF.
- Experience building network configuration and control systems (e.g., based on SONiC, gNMI, Netconf).
Job Information
【For Pay Transparency】Compensation Description (Annually)
The base salary range for this position in the selected city is $128000 \- $256000 annually.
Compensation may vary outside of this range depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.
Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short\-term and long\-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).
The Company reserves the right to modify or change these benefits programs at any time, with or without notice.
For Los Angeles County (unincorporated) Candidates:
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:
1\. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
2\. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
3\. Exercising sound judgment.
About Us
Founded in 2012, ByteDance's mission is to inspire creativity and enrich life. With a suite of more than a dozen products, including TikTok, Lemon8, CapCut and Pico as well as platforms specific to the China market, including Toutiao, Douyin, and Xigua, ByteDance has made it easier and more fun for people to connect with, consume, and create content.
Why Join ByteDance
Inspiring creativity is at the core of ByteDance's mission. Our innovative products are built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and enrich life \- a mission we work towards every day.
As ByteDancers, we strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our Company, and our users. When we create and grow together, the possibilities are limitless. Join us.
Diversity \& Inclusion
ByteDance is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At ByteDance, our mission is to inspire creativity and enrich life. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.
Reasonable Accommodation
ByteDance is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at
https://tinyurl.com/RA\-request
Salary Context
This $128K-$256K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At ByteDance, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($192K) sits 11% below the category median. Disclosed range: $128K to $256K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
ByteDance AI Hiring
ByteDance has 26 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, AI Software Engineer, Research Engineer. Positions span Seattle, WA, US, San Diego, CA, US, San Jose, CA, US. Compensation range: $151K - $480K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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