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Machine Learning Engineer Jobs in Albany, CA (NOW HIRING)

Zoomcar

Machine Learning Engineer

San Francisco, CA · On-site

$150 - $190/hr

The Opportunity We are building a platform for AI Agents to come together and solve arbitrarily complex tasks, leveraging Superhuman ubiquitous UI. As a Machine Learning Engineer on this team, you ...

Reducto

Machine Learning Engineer

San Francisco, CA · On-site

They are seeking a Machine Learning Engineer to train and deploy critical models for their core product, focusing on interpreting unstructured data and improving model performance. Responsibilities ...

Menlo Ventures

Machine Learning Engineer

San Mateo, CA · On-site

$195 - $350/hr

We're hiring a Machine Learning Engineer as the volume and complexity of legal AI workflows in our system scale rapidly. As more firms rely on Eve to automate high‑stakes legal work -- from intake ...

Superhuman

Machine Learning Engineer

San Francisco, CA · On-site

$180 - $260/hr

The Opportunity We are building a platform for AI Agents to come together and solve arbitrarily complex tasks, leveraging Superhuman ubiquitous UI. As a Machine Learning Engineer on this team, you ...

Canals

Machine Learning Engineer

San Francisco, CA · Remote

We're looking for a senior-level Machine Learning Engineer who can move quickly while maintaining high quality, owning end-to-end ML pipelines while shaping product features that deliver real-world ...

Goodfire

Machine Learning Engineer

San Francisco, CA

$200K - $400K/yr

About the role We're looking for Machine Learning Engineers to help build our platform for training, evaluating, and deploying interpretable AI systems at scale. You'll play a central role in ...

Relace

Machine Learning Engineer

San Francisco, CA · On-site

The Role We're looking for a Machine Learning Engineer who loves getting close to the metal. This is a hands-on engineering role focused on making models faster, more efficient, and more reliable ...

Superhuman

Machine Learning Engineer

San Francisco, CA · On-site

$250K - $385K/yr

The Opportunity We are building a platform for AI Agents to come together and solve arbitrarily complex tasks, leveraging Superhuman ubiquitous UI. As a Machine Learning Engineer on this team, you ...

Clay Labs

Machine Learning Engineer

San Francisco, CA · On-site

$170K - $300K/yr

Machine Learning Engineer @ Clay Clay's ambition is to build a self-learning revenue engine : a product that gets smarter every time someone uses it. This means data, ML, and AI are at the heart of ...

Sift

Machine Learning Engineer

San Francisco, CA · On-site

$160 - $230/hr

As a Machine Learning Engineer at Sift, you will bridge the gap between data science and large-scale distributed systems. You won't just train models in isolation; you will build end-to-end pipelines ...

Clay Labs

Machine Learning Engineer

San Francisco, CA · On-site

$180 - $240/hr

Machine Learning Engineer @ Clay Clay's ambition is to build a self-learning revenue engine : a product that gets smarter every time someone uses it. This means data, ML, and AI are at the heart of ...

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Showing results 41-60

All JobsMachine Learning Engineer JobsMachine Learning Engineer Jobs in Albany, CA

Machine Learning Engineer information

See Albany, CA salary details

$36.8K

$150.6K

$226.3K

How much do machine learning engineer jobs pay per year?

As of Aug 23, 2026, the average yearly pay for machine learning engineer in Albany, CA is $150,627.00, according to ZipRecruiter salary data. Most workers in this role earn between $118,700.00 and $181,300.00 per year, depending on experience, location, and employer.

What is a machine learning engineer?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What does a machine learning engineer do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are the key skills and qualifications needed to thrive as a machine learning engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What are some common challenges faced by machine learning engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

More about Machine Learning Engineer jobs

What job categories do people searching Machine Learning Engineer jobs in Albany, CA look for?

The top searched job categories for Machine Learning Engineer jobs in Albany, CA are:

What cities near Albany, CA are hiring for Machine Learning Engineer jobs?

Cities near Albany, CA with the most Machine Learning Engineer job openings:

Machine Learning Engineer jobs near you
Infographic showing various Machine Learning Engineer job openings in Albany, CA as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 21% Part Time, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $150,627 per year, or $72.4 per hour.

Machine Learning Engineer

Human Archive

San Francisco, CA • On-site

$100K - $150K/yr

Full-time

Re-posted 2 days ago

Job description

About Human Archive
Human Archive is a research lab focused on modeling human embodied intelligence.
Humans are the most sophisticated biological systems we have ever observed, yet we still do not fully understand ourselves. Research into human physical intelligence - including the human hand, proprioception, and vision - remains largely unsolved. Our mission is to recover human embodied intelligence as a learned model. To achieve this, we build custom hardware products, deploy them globally at scale, and publish research. Today, our data is used for robotics and world modeling, but the broader opportunity is advancing scientific research into intelligence itself.
Founded by Stanford and UC Berkeley researchers, we are lean, deeply technical, and operate at extreme speed, taking on unglamorous and conventionally impossible problems that directly unlock step-function gains in model capability.
The deployment of capable humanoids at scale will permanently redefine human labor. Undesirable physical work will disappear, and human effort will shift toward a new era of abundant creativity.
We are building the infrastructure to accelerate that transition by assembling the Human Archive mafia. You will own meaningful systems from day one and see your work directly impact model capabilities. This is a once-in-a-generation inflection point. If you want to help reshape physical labor and work on problems that matter at civilizational scale, join us.
The Opportunity
As a Machine Learning Engineer, you'll work on multimodal perception, VLA training, robotics post-training, and downstream policy evaluation. This is a hands-on role at the intersection of applied machine learning, data infrastructure, and robotics, where your work directly shapes how data is collected, validated, annotated, and evaluated.
You'll help close the loop between research and data collection by fine-tuning VLAs on downstream policy performance and building post-training and reinforcement learning systems around real-world robotics tasks. You'll be expected to make architectural decisions, own projects end-to-end, and operate in highly ambiguous research environments given the novelty and scale of our multimodal datasets.
Your work will help shape how frontier labs and leading robotics companies train their models, transforming physical labor markets and economies while contributing to broader research into human embodied intelligence.
What You'll Do
  • Build systems for multimodal perception, annotation, dataset QA, and robotics evaluation
  • Publish research on multimodal data by fine-tuning and evaluating VLA models on downstream robotics tasks and policy performance
  • Build post-training and reinforcement learning systems around robotics failure modes and corrective demonstrations
  • Work across video understanding, tracking, pose estimation, temporal modeling, and multimodal alignment
  • Develop tooling for benchmarking, observability, and temporal efficiency
  • Prototype quickly, ship rapidly, and iterate from real-world robotics deployments and research feedback
What We're Looking For
  • Passionate, mission-driven individuals who have demonstrated exceptional ownership in previous work
  • Engineers who want their work to directly impact the next frontier of physical AGI
  • Strong ML engineering fundamentals across robotics, computer vision, and perception systems
  • Experience with video understanding, tracking, pose estimation, robotics, or real-world sensor systems
  • Strong technical intuition and ability to move quickly in ambiguous research environments
  • Published research or production experience in robotics, embodied AI, reinforcement learning, motion capture, or vision systems is a strong plus

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