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Multimodal Learning Jobs in Boston, MA (NOW HIRING)

Abbott

Staff AI/ML Engineer

Westford, MA · On-site

$99K - $198K/yr

Experience with foundation models, generative AI, self-supervised learning, multimodal learning, or other advanced AI approaches applied to medical imaging. * Expertise in model deployment and ...

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All JobsMultimodal Learning JobsMultimodal Learning Jobs in Boston, MA

Multimodal Learning information

See Boston, MA salary details

$22.8K

$67K

$124.4K

How much do multimodal learning jobs pay per year?

As of Aug 31, 2026, the average yearly pay for multimodal learning in Boston, MA is $67,022.00, according to ZipRecruiter salary data. Most workers in this role earn between $44,500.00 and $78,200.00 per year, depending on experience, location, and employer.

What is multimodal learning?

Multimodal learning is an area of machine learning that involves integrating and processing information from multiple types of data, such as text, images, audio, and video. The goal is to create models that can understand and make predictions based on more than one data modality, similar to how humans use various senses. This approach is used in applications like speech recognition with visual cues, image captioning, and video analysis. By combining different data types, multimodal learning systems can achieve better accuracy and more robust understanding.

What are the key skills and qualifications needed to thrive in multimodal learning, and why are they important?

To excel as a Multimodal Learning Specialist, you need a solid background in machine learning, data science, and computer vision, often supported by an advanced degree in a related field. Familiarity with deep learning frameworks like TensorFlow or PyTorch, experience integrating data from diverse sources (e.g., text, audio, images), and knowledge of relevant algorithms are crucial. Strong problem-solving abilities, creativity, and effective collaboration are standout soft skills for this role. These competencies are vital for developing innovative models that can process and interpret complex, multi-source data to drive impactful AI solutions.

What are some common challenges faced by professionals working in multimodal learning roles, and how can they be addressed?

Professionals in multimodal learning frequently encounter challenges related to integrating and aligning data from multiple sources, such as text, images, audio, or video. Ensuring data quality and consistency across modalities can be complex, and developing models that effectively combine heterogeneous information often requires advanced technical skills and innovative thinking. Collaboration with domain experts and other data scientists is key to overcoming these obstacles, as is staying up to date with the latest research and tools in machine learning. Regular team meetings and cross-disciplinary workshops can help foster a collaborative environment and promote knowledge sharing.

What is the difference between Multimodal Learning vs Data Scientist?

AspectMultimodal LearningData Scientist
Required CredentialsAdvanced degrees in AI, Machine Learning, or Computer ScienceBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, AI development teams, academiaBusiness, tech companies, analytics teams
Industry UsageAI research, multimedia applications, roboticsData analysis, predictive modeling, business insights

Multimodal Learning focuses on developing AI models that process and integrate multiple data types like images, text, and audio. Data Scientists analyze data to extract insights, build models, and support decision-making. While both roles involve data and algorithms, Multimodal Learning is specialized in AI model development for complex data integration, whereas Data Scientists work broadly across data analysis and interpretation.

What are popular job titles related to Multimodal Learning jobs in Boston, MA?

For Multimodal Learning jobs in Boston, MA, the most frequently searched job titles are:

What job categories do people searching Multimodal Learning jobs in Boston, MA look for?

The top searched job categories for Multimodal Learning jobs in Boston, MA are:

What cities near Boston, MA are hiring for Multimodal Learning jobs?

Cities near Boston, MA with the most Multimodal Learning job openings:

Multimodal Learning jobs near you
Infographic showing various Multimodal Learning job openings in Boston, MA 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 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $67,022 per year, or $32.2 per hour.

Scientist II / Senior ML Scientist, Data-Efficient Learning for Drug Discovery

Lila Sciences

Cambridge, MA • On-site

Full-time

Medical, Dental, Vision, Life

Posted 28 days ago

Job description

Your Impact at LILA
Lila Sciences is seeking a Machine Learning Scientist, Data-Efficient Learning for Drug Discovery to build models and learning strategies for settings where data is scarce, expensive, and intentionally generated. This role is focused on training useful models from low-quantity but high-quality datasets ranging from as few as tens to low thousands of examples, often in tightly focused areas of chemical space, and deciding what data should be acquired next.
This is an applied scientific ML role in a frontier research area. The work is not a matter of applying standard models out of the box. You will use and develop approaches across active learning, meta-learning, fine-tuning, uncertainty estimation, experimental design, and multimodal modeling to help Lila build closed-loop systems that learn efficiently from targeted data acquisition.
This role connects model training with scientific decision-making: data acquisition plans should be useful to computational chemists evaluating compound priorities, computational biophysicists deciding when simulation is warranted, and cofolding modelers deciding which protein-ligand data would improve structure-aware models.
What You'll Be Building
  • Build ML models that perform well in low-data regimes for drug discovery and molecular optimization.
  • Design data acquisition strategies that identify which compounds, assays, DEL selections, simulations, structural predictions, or experiments should be run next to maximize learning.
  • Develop active learning, meta-learning, fine-tuning, transfer learning, and uncertainty-aware modeling approaches for focused chemical spaces.
  • Train models on low-quantity, high-quality datasets generated by Lila's experimental, computational, and agentic discovery systems.
  • Build multimodal models that can integrate DEL data, simulation outputs, assay data, protein and structural information, chemical features, literature or text-derived signals, images, and experimental metadata.
  • Partner with experimental, computational, and drug discovery teams to ensure data acquisition plans are scientifically meaningful and operationally feasible.
  • Evaluate models through learning curves, prospective validation, retrospective benchmarks, uncertainty calibration, and decision-focused metrics.
  • Develop closed-loop learning workflows that continuously update models as new data arrives from experiments, simulations, and automated systems.
  • Translate model predictions and uncertainty into practical recommendations for compound selection, assay selection, batch design, or next experiments.
  • Work with platform and agent teams to expose model-driven recommendations as tools for scientists and AI agents.

What You'll Need to Succeed
  • PhD or equivalent experience in machine learning, computational chemistry, computational biology, statistics, computer science, bioengineering, or a related field.
  • Strong experience training ML models in low-data regimes.
  • Experience with active learning, Bayesian optimization, experimental design, meta-learning, fine-tuning, transfer learning, uncertainty estimation, or related data-efficient learning methods.
  • Experience building ML models for scientific, molecular, biological, chemical, pharmacological, biochemical, or other high-dimensional experimental datasets.
  • Experience with multimodal learning or methods that combine heterogeneous data sources.
  • Ability to reason about data acquisition strategy, not only model fitting.
  • Strong scientific judgment and ability to connect model behavior to experimental decisions.
  • Practical experience with PyTorch, JAX, scikit-learn, or equivalent ML tools.
  • Ability to collaborate across ML, data, computational science, experimental, and drug discovery teams.

Bonus Points For
  • Drug discovery experience, especially in molecular optimization, screening, or design-make-test-learn workflows.
  • General understanding of pharmacology, biochemistry, or mechanisms of molecular activity.
  • Experience with DEL, high-throughput screening, medicinal chemistry, assay data, simulation-derived features, protein or structure-based features, text or literature features, or scientific images.
  • Experience with closed-loop experimentation, autonomous labs, or agent-driven scientific workflows.
  • Experience with generative molecular design, candidate prioritization, or batch selection workflows.
  • Familiarity with causal inference, optimal experimental design, decision theory, or Bayesian methods.
  • Comfort working with frontier ML techniques where standard out-of-the-box approaches are insufficient.

Compensation
We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.
U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.
International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.
Expected Base Salary Range
$228,000-$358,000 USD
About LILA
Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.
Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.
We're All In
Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.
A Note to Agencies
Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.

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