Mid Level Machine Learning Jobs
Mid level machine learning jobs go to engineers ready to own model development end to end, make architectural decisions with limited oversight, and guide junior teammates through production challenges. Openings run across 37% remote and hybrid settings in Technology & Software, Electronics & Hardware, and Banking & Financial Services, with employers like Apple, Scale AI, and General Motors hiring at this level now.
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Showing 5 of 396+ Mid Level Machine Learning jobs
Responsibilities
We are a group of applied machine learning engineers that focus on TikTok recommendations and search engineers powering multiple product areas such as For-You-Page (FYP), Live Streaming, Global E-commerce, Local Services and more. We are developing innovative algorithms and techniques to improve user engagement and satisfaction, converting creative ideas into business-impacting solutions. We are interested in and excited about pushing the envelope of State-of-the-Art (SOTA) large scale machine learning to solve various real-world problems.
What You'll Do
- Technical Leadership: Drive the technical roadmap for our large-scale (in billions parameters) distributed real-time ML training and inferencing platforms that power the TikTok recommendation and search engines, Short Form Video (SFV) ecosystem. Have a direct business impact on Live, Global E-Commerce, Local Services and many businesses domains.
- Large-Scale Parallelism Architecture: Architect and scale multi-node distributed training systems, implementing advanced 3D parallelism strategies (Data, Tensor, Pipeline) to maximize compute efficiency and model scalability. Lead the architecture, scale-testing, and maintenance of massive distributed computing foundations, GPU cluster configurations, and orchestration pipelines, establish robust SLI/SLO frameworks while maximizing hardware utilization and cluster efficiency to expedite innovation.
- Production Inference & Serving: Build and scale low-latency, high-throughput model serving infrastructure, optimizing inference pipelines and leveraging low-level execution paths to handle massive live traffic under strict boundary isolation.
- Algorithm-Infrastructure Co-Design: Partner closely with Applied ML Research teams to co-design and pioneer next-generation Generative Recommendation systems, abstracting general-purpose components to support advanced generative paradigms in production.
- Resiliency & Fault Tolerance: Design robust, automated fault-detection systems and asynchronous checkpointing mechanisms to gracefully handle hardware drops, silent data corruption (SDC), or network-switch failures in multi-thousand GPU clusters.
- Cross-Functional Collaboration: Partner with Applied ML researchers and data platform teams to engineer high-throughput, secure multi-modal data processing and storage engines that prevent compliance friction.
- Security & Compliance Hardening: Implement and enforce strict encryption-at-rest/in-transit controls, access control lists (ACLs), and secure tenant isolation protocols across the entire compute stack to meet compliance objectives.
Qualifications
Minimum Qualifications
- Bachelor’s or Master's degree in Computer Science, Computer Engineering, or a related technical discipline.
- 5+ years of professional software engineering experience with deep expertise in Python, C++/Java and a proven track record of designing large-scale distributed systems.
- 3+ years of direct experience building and maintaining machine learning infrastructure at enterprise scale (managing large GPU clusters, Kubernetes, or native Slurm environments).
- Deep technical familiarity with the internals of core ML frameworks (PyTorch / Tensorflow) and a strong understanding of low-level GPU memory management, CUDA interactions, and networking topologies (InfiniBand/RoCE).
- Solid understanding of production-grade LLM training and inference tools, with hands-on profiling skills to eliminate I/O, compute, or network bottlenecks.
- Strong system-level troubleshooting and debugging skills, with experience profiling and eliminating I/O, compute, or network bottlenecks.
Preferred Qualifications
- Experience optimizing high-performance training loops to maximize Model Flops Utilization (MFU) through advanced communication-computation overlap and zero-bubble pipeline scheduling across large-scale distributed clusters using industry-standard frameworks (e.g., Megatron, DeepSpeed).
- Proven track record of scaling LLM training or inference workloads across hundreds of GPUs, with deep familiarity in advanced serving techniques such as KV Cache management, Prefill-Decoding (PD) separation, and model quantization.
- Experience working in highly regulated industries, sovereign cloud environments, or dealing with federal data security compliance frameworks.
- Strong flavor in low-level kernel development and graph compilation, with experience in CUDA, Triton, Cutlass, TensorRT, or Triton Inference Server being a huge plus.
- Active background or interest in keeping up with the latest industry breakthroughs in MLOps, MoE (Mixture of Experts) routing infrastructure, and specialized hardware optimization.
- Excellent technical leadership skills, with the ability to mentor junior engineers, draft clear architecture designs, and communicate complex infrastructure needs to non-technical stakeholders.
About USDS
TikTok USDS Joint Venture LLC is dedicated to the safety and security of millions of Americans who create, discover, and connect with what they love on the apps we operate. The Joint Venture has been established in compliance with the Executive Order signed by President Trump on September 25, 2025. Our foundation is a comprehensive data privacy and cybersecurity program we operate under defined safeguards to protect national security and secure U.S. user data, apps and the algorithm. We safeguard the U.S. content ecosystem, holding decision-making authority for trust and safety policies and moderation. USDS Joint Venture helps ensure Americans can continue to express their creativity, discover new hobbies and interests, and build thriving communities and businesses on a global scale.
On-site presence across teams allows the company to operate with greater speed, alignment, and agility — especially in areas like real-time decision-making, team development, and integrated execution. As such, the company is shifting from a hybrid work model to a fully in-person schedule up to 5 days a week.
Why Join Us
Inspiring creativity is at the core of TikTok's mission. Our innovative product is 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 bring joy – a mission we work towards every day. 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. Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come. 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
TikTok 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 TikTok, our mission is to inspire creativity and bring joy. 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.
USDS Reasonable Accommodation
USDS 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/USDS-RA
Job Information
【For Pay Transparency】Compensation Description (Annually) The base salary range for this position in the selected city is $198360 - $416100 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.
Responsibilities
We are a group of applied machine learning engineers that focus on TikTok recommendations and search engineers powering multiple product areas such as For-You-Page (FYP), Live Streaming, Global E-commerce, Local Services and more. We are developing innovative algorithms and techniques to improve user engagement and satisfaction, converting creative ideas into business-impacting solutions. We are interested in and excited about pushing the envelope of State-of-the-Art (SOTA) large scale machine learning to solve various real-world problems.
What You'll Do
- Technical Leadership: Drive the technical roadmap for our large-scale (in billions parameters) distributed real-time ML training and inferencing platforms that power the TikTok recommendation and search engines, Short Form Video (SFV) ecosystem. Have a direct business impact on Live, Global E-Commerce, Local Services and many businesses domains.
- Large-Scale Parallelism Architecture: Architect and scale multi-node distributed training systems, implementing advanced 3D parallelism strategies (Data, Tensor, Pipeline) to maximize compute efficiency and model scalability. Lead the architecture, scale-testing, and maintenance of massive distributed computing foundations, GPU cluster configurations, and orchestration pipelines, establish robust SLI/SLO frameworks while maximizing hardware utilization and cluster efficiency to expedite innovation.
- Production Inference & Serving: Build and scale low-latency, high-throughput model serving infrastructure, optimizing inference pipelines and leveraging low-level execution paths to handle massive live traffic under strict boundary isolation.
- Algorithm-Infrastructure Co-Design: Partner closely with Applied ML Research teams to co-design and pioneer next-generation Generative Recommendation systems, abstracting general-purpose components to support advanced generative paradigms in production.
- Resiliency & Fault Tolerance: Design robust, automated fault-detection systems and asynchronous checkpointing mechanisms to gracefully handle hardware drops, silent data corruption (SDC), or network-switch failures in multi-thousand GPU clusters.
- Cross-Functional Collaboration: Partner with Applied ML researchers and data platform teams to engineer high-throughput, secure multi-modal data processing and storage engines that prevent compliance friction.
- Security & Compliance Hardening: Implement and enforce strict encryption-at-rest/in-transit controls, access control lists (ACLs), and secure tenant isolation protocols across the entire compute stack to meet compliance objectives.
Qualifications
Minimum Qualifications
- Bachelor’s or Master's degree in Computer Science, Computer Engineering, or a related technical discipline.
- 5+ years of professional software engineering experience with deep expertise in Python, C++/Java and a proven track record of designing large-scale distributed systems.
- 3+ years of direct experience building and maintaining machine learning infrastructure at enterprise scale (managing large GPU clusters, Kubernetes, or native Slurm environments).
- Deep technical familiarity with the internals of core ML frameworks (PyTorch / Tensorflow) and a strong understanding of low-level GPU memory management, CUDA interactions, and networking topologies (InfiniBand/RoCE).
- Solid understanding of production-grade LLM training and inference tools, with hands-on profiling skills to eliminate I/O, compute, or network bottlenecks.
- Strong system-level troubleshooting and debugging skills, with experience profiling and eliminating I/O, compute, or network bottlenecks.
Preferred Qualifications
- Experience optimizing high-performance training loops to maximize Model Flops Utilization (MFU) through advanced communication-computation overlap and zero-bubble pipeline scheduling across large-scale distributed clusters using industry-standard frameworks (e.g., Megatron, DeepSpeed).
- Proven track record of scaling LLM training or inference workloads across hundreds of GPUs, with deep familiarity in advanced serving techniques such as KV Cache management, Prefill-Decoding (PD) separation, and model quantization.
- Experience working in highly regulated industries, sovereign cloud environments, or dealing with federal data security compliance frameworks.
- Strong flavor in low-level kernel development and graph compilation, with experience in CUDA, Triton, Cutlass, TensorRT, or Triton Inference Server being a huge plus.
- Active background or interest in keeping up with the latest industry breakthroughs in MLOps, MoE (Mixture of Experts) routing infrastructure, and specialized hardware optimization.
- Excellent technical leadership skills, with the ability to mentor junior engineers, draft clear architecture designs, and communicate complex infrastructure needs to non-technical stakeholders.
About USDS
TikTok USDS Joint Venture LLC is dedicated to the safety and security of millions of Americans who create, discover, and connect with what they love on the apps we operate. The Joint Venture has been established in compliance with the Executive Order signed by President Trump on September 25, 2025. Our foundation is a comprehensive data privacy and cybersecurity program we operate under defined safeguards to protect national security and secure U.S. user data, apps and the algorithm. We safeguard the U.S. content ecosystem, holding decision-making authority for trust and safety policies and moderation. USDS Joint Venture helps ensure Americans can continue to express their creativity, discover new hobbies and interests, and build thriving communities and businesses on a global scale.
On-site presence across teams allows the company to operate with greater speed, alignment, and agility — especially in areas like real-time decision-making, team development, and integrated execution. As such, the company is shifting from a hybrid work model to a fully in-person schedule up to 5 days a week.
Why Join Us
Inspiring creativity is at the core of TikTok's mission. Our innovative product is 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 bring joy – a mission we work towards every day. 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. Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come. 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
TikTok 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 TikTok, our mission is to inspire creativity and bring joy. 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.
USDS Reasonable Accommodation
USDS 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/USDS-RA
Job Information
【For Pay Transparency】Compensation Description (Annually) The base salary range for this position in the selected city is $198360 - $416100 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.
See All 396+ Mid Level Machine Learning Jobs
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Find JobsMid Level Machine Learning Job Market
Who's Hiring
- Apple74
- Scale AI15
- General Motors14
- Amazon13
- Waymo9
Top Industries Hiring
- Technology & Software162
- Electronics & Hardware87
- Banking & Financial Services42
- Automotive25
- Artificial Intelligence22
Mid Level Machine Learning Jobs: Frequently Asked Questions
How do I get a mid level machine learning job?
Position yourself around ownership, not just contribution. Highlight projects where you drove a model from research through deployment, made design trade-offs, and measured real-world impact. Tailor your resume to show depth in a specific area, such as NLP, computer vision, or recommendation systems, and demonstrate that you can operate with limited day-to-day direction. That combination is what mid level hiring managers screen for.
Which companies hire mid level machine learnings?
Companies hiring mid level machine learnings right now include Apple, Scale AI, and General Motors, based on current listings on Migrate Mate as of August 2026. Hiring at this level comes from a wide range of organizations, including established tech companies, growth-stage startups scaling their AI capabilities, and large enterprises building out dedicated applied machine learning teams.
Are there remote mid level machine learning jobs?
Yes, and flexibility at this level is common. About 37% of mid level machine learning openings are remote or hybrid as of August 2026, reflecting how well the work translates to distributed teams. Fully on-site roles do exist, typically at companies with strict data security requirements or where close collaboration with hardware and infrastructure teams is essential.
How do I move up to a mid level machine learning role?
The path from entry level to mid level is built on demonstrated ownership over time. Engineers who advance focus on shipping models to production rather than only running experiments, take on progressively larger scopes with less guidance, and develop a specialty where they can go deep. Documenting measurable outcomes, such as accuracy improvements or latency reductions, builds the portfolio that makes the jump credible to hiring teams.
Which industries hire the most mid level machine learnings?
Mid Level machine learning roles concentrate in Technology & Software, Electronics & Hardware, and Banking & Financial Services, based on current listings on Migrate Mate as of August 2026. These sectors drive hiring at this level because they have both the data volume and the product complexity that make mid level ML expertise, rather than generalist software engineering, the right fit for their core technical challenges.