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About This Role
Amide Technologies is a Massachusetts\-based biotech company that designs therapeutic peptides and proteins which are difficult to obtain by conventional means, combining solid\-phase peptide synthesis with biological expression. By using non\-natural amino acids and artificial protein backbones, we design peptides that conventional chemistry and biology cannot reach. Because Amide makes compounds that have never been made before, the work regularly involves problems with no established playbook.
We are seeking an AI/ML engineer to build the platform and agent layer that connects scientific data, internal models and experimental decision\-making. This role will focus on making Amide’s AI/ML capability usable by scientists through reliable data flows, model\-ready datasets, internal agents, workflow orchestration and interfaces to ELN/LIMS and laboratory systems. You will join a multidisciplinary environment at our Waltham, Massachusetts site and work closely with biology, chemistry, lab operations, data science and software colleagues.
Role description:
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The qualified individual will have recognized expertise in AI/ML engineering, scientific software, data integration and applied automation. The candidate will help convert fragmented experimental and computational workflows into an internal platform that supports prospective peptide design, assay interpretation, and program decisions. Infrastructure operations may be supported by vendors and contractors, but the scientific workflow logic, data model, and user\-facing AI tools must be owned and shaped internally.
In this role you will:
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- Build internal AI agents and workflow tools that help scientists query data, interpret model outputs and make design decisions.
- Integrate synthesis, assay, ELN/LIMS, and computational data into model\-ready layers that can support repeatable learning loops.
- Orchestrate lab\-in\-the\-loop modeling workflows that connect design, synthesis, testing, analysis and next\-round recommendation.
- Partner with lab automation and scientific software colleagues to improve data capture, traceability, and handoffs between experimental and computational teams.
- Build applications, APIs and data services that expose ML capabilities to scientific users.
- Work with outsourced infrastructure, MLOps, or data\-engineering partners while maintaining strong internal ownership of scientific requirements and platform behavior.
- Contribute to platform governance, documentation, reproducibility and practical standards for model use in project decisions.
Required Experience and Skills:
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- AI/ML Engineer, Scientific Software Engineer, or Platform Engineer with a degree in Computer Science, Bioengineering, Data Science or a related discipline.
- Strong programming skills in Python and experience building production\-quality scientific software, data pipelines, APIs or internal workflow tools.
- Experience building AI agents, LLM\-enabled scientific tools, workflow orchestration systems and model serving applications.
- Familiarity with biological, chemical, assay or laboratory data and the practical challenges of making such data usable for ML.
- Good judgment about build\-versus\-buy tradeoffs, vendor management and how to use outsourced infrastructure without losing scientific control.
- Experience working in matrixed teams of scientists, engineers and operations colleagues to meet project objectives.
- Demonstrated effective problem\-solving skills, strong organization and clear communication across scientific and technical teams.
- Experience integrating data from databases, APIs, ELN/LIMS systems, instrument outputs and cloud data stores into reliable internal applications.
Preferred Experience and Skills:
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- Experience with MLOps, model\-serving patterns, feature stores, metadata tracking or reproducible ML workflow infrastructure.
- Experience building scientist\-facing tools in pharma, diagnostics, synthetic biology, chemistry, lab automation.
- Ability to define a platform data model that supports synthesis, assay, sequence, structure, target and program\-level decision workflows.
- Experience scaling a greenfield scientific platform from early prototypes to repeatable workflows used across multiple discovery programs.
The position is full time and will be based at the company’s headquarters in Waltham, Massachusetts, USA. Flexibility with regard to working hours is required. The AI/ML Engineer, Platform and Agents will report to the Chief Data Science Officer.
Amide Technologies offers a challenging and exciting role in one of the Northeast’s most innovative biotech companies. The company offers a competitive compensation package including salary, bonus and equity.
Amide deeply values diversity and is committed to creating an inclusive environment for all employees. We are an equal opportunity employer. We consider all qualified applicants equally for employment. We do not discriminate on the basis of race, color, national origin, ancestry, citizenship status, protected veteran status, religion, physical or mental disability, marital status, sex, sexual orientation, gender identity or expression, age, or any other basis protected by law, ordinance, or regulation.
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 Amide Technologies, 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.
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.
Amide Technologies AI Hiring
Amide Technologies has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Waltham, MA, US.
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.
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