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Applied Machine Learning Jobs (NOW HIRING)

Respondology

Senior Applied Machine Learning Engineer

$140K - $175K/yr

About the role: We're hiring a Senior Applied Machine Learning Engineer to join the small team that makes AI work tractable, safe, and fast across the company. In this role, you'll ship LLM-powered ...

Alljoined

Machine Learning Researcher

San Francisco, CA · On-site

$140K - $250K/yr

... applied machine learning engineering; or * A graduate degree (M.S. or Ph.D.) in computer science or a related field-such as artificial intelligence, computational neuroscience, or biomedical ...

LHi Group Ltd

Staff Machine Learning Engineer

Calumet, PA · On-site

$280K/yr

* Staff Applied Machine Learning Engineer * Remote (must be based in USA) * Work Authorization: ship or required due to government contract requirements * $230-280,000 base + Equity + Benefits About ...

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$25.5K

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How much do applied machine learning jobs pay per year?

As of Aug 10, 2026, the average yearly pay for applied machine learning in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What are the typical collaboration dynamics between applied machine learning engineers and other teams within a company?

Applied Machine Learning engineers often work closely with cross-functional teams including data scientists, software engineers, product managers, and business analysts. They are typically responsible for translating business problems into machine learning solutions and ensuring models are effectively integrated into production systems. This role requires frequent communication to align on project goals, share progress, and address technical challenges, making teamwork and stakeholder management crucial for successful deployments and continuous improvement.

What is applied machine learning?

Applied machine learning involves using machine learning techniques and algorithms to solve real-world problems in various industries, such as healthcare, finance, and technology. Practitioners focus on selecting appropriate models, preparing data, training algorithms, and deploying solutions that deliver tangible value. Unlike theoretical machine learning, applied machine learning emphasizes practical implementation, evaluation, and optimization to meet business or research objectives.

What are the key skills and qualifications needed to thrive as an applied machine learning professional?

To excel in Applied Machine Learning, you need a solid background in mathematics, statistics, computer science, and experience with machine learning algorithms, often supported by a relevant degree or certification. Familiarity with programming languages like Python or R, frameworks such as TensorFlow or PyTorch, and version control systems is typically required. Strong problem-solving abilities, communication skills, and a collaborative mindset help you interpret results and convey insights to diverse stakeholders. These competencies are crucial for building effective models, driving data-driven decisions, and ensuring the successful integration of machine learning solutions into real-world applications.
More about Applied Machine Learning jobs
What cities are hiring for Applied Machine Learning jobs? Cities with the most Applied Machine Learning job openings:
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Applied Machine Learning jobs near you
Infographic showing various Applied Machine Learning job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Applied Machine Learning Engineer

Inference

San Francisco, CA • On-site

$220K - $320K/yr

Full-time

Re-posted 6 days ago

Job description

Help us build the systems that train specialized AI models for the fastest-growing companies in the world. If you love taking cutting-edge ML techniques and turning them into products that ship, we'd love to meet you.
About Inference.net
Inference.net trains and hosts specialized language models for companies who want frontier-quality AI at a fraction of the cost. The models we train match GPT-5 accuracy but are smaller, faster, and up to 90% cheaper. Our platform handles everything end-to-end: distillation, training, evaluation, and planet-scale hosting.
We are a well-funded ten-person team of engineers who work in-person in downtown San Francisco on difficult, high-impact engineering problems. Everyone on the team has been writing code for over 10 years, and has founded and run their own software companies. We are high-agency, adaptable, and collaborative. We value creativity alongside technical prowess and humility. We work hard, and deeply enjoy the work that we do. Most of us are in the office 4 days a week in SF; hybrid works for Bay Area candidates.
About the Role
You will be responsible for building and improving the core ML systems that power our custom model training platform, while also applying these systems directly for customers. Your role sits at the intersection of applied research and production engineering. You'll lead projects from data intake to trained model, building the infrastructure and tooling along the way.
Your north star is model quality at scale, measured by how well our custom models match frontier performance, how efficiently we can train and serve them, and how smoothly we can deliver results to our customers. You'll own the full training lifecycle: processing data, creating dashboards for visibility, training models using our frameworks, running evaluations, and shipping results. This role reports directly to the founding team. You'll have the autonomy, a large compute budget / GPU reservation, and technical support to push the boundaries of what's possible in custom model training.
Key Responsibilities
  • Lead projects from from data intake through the full training pipeline, including processing, cleaning, and preparing datasets for model training
  • Build and maintain data processing pipelines for aggregating, transforming, and validating training data
  • Create dashboards and visualization tools to display training metrics, data quality, and model performance
  • Train models using our internal frameworks and iterate based on evaluation results
  • Develop robust benchmarks and evaluation frameworks that ensure custom models match or exceed frontier performance
  • Build systems to automate portions of the training workflow, reducing manual intervention and improving consistency
  • Take research features and ship them into production settings
  • Apply the latest techniques in SFT, RL, and model optimization to improve training quality and efficiency
  • Collaborate with infrastructure engineers to scale training across our GPU fleet
  • Deeply understand customer use cases to inform training strategies and surface edge cases

Requirements
  • 2+ years of experience training AI models using PyTorch
  • Hands-on experience with post-training LLMs using SFT or RL
  • Strong understanding of transformer architectures and how they're trained
  • Experience with LLM-specific training frameworks (e.g., Hugging Face Transformers, DeepSpeed, Axolotl, or similar)
  • Experience training on NVIDIA GPUs
  • Strong data processing skills and comfortable building ETL pipelines and working with large datasets
  • Track record of creating benchmarks and evaluations
  • Ability to take research techniques and apply them to production systems

Nice-to-Have
  • Experience with model distillation or knowledge transfer
  • Experience building dashboards and data visualization tools
  • Familiarity with vision encoders and multimodal models
  • Experience with distributed training at scale
  • Contributions to open-source ML projects

You don't need to tick every box. Curiosity and the ability to learn quickly matter more.
Compensation
We offer competitive compensation, equity in a high-growth startup, and comprehensive benefits. The base salary range for this role is $220,000 - $320,000, plus equity and benefits, depending on experience.
Equal Opportunity
Inference.net is an equal opportunity employer. We welcome applicants from all backgrounds and don't discriminate based on race, color, religion, gender, sexual orientation, national origin, genetics, disability, age, or veteran status.
If you're excited about building the future of custom AI infrastructure, we'd love to hear from you. Please send your resume and GitHub to amar@inference.net and/or apply here on Ashby.

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